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Int. J. Indian Culture and Business Management, Vol. X, No. X, xxxx 1
Copyright © 200x Inderscience Enterprises Ltd.
Location strategies of dairy plants in India
Tejinder Sharma*
Department of Commerce,
Kurukshetra University,
Kurukshetra 136119, Haryana, India
E-mail: sharmatejinder@gmail.com
*Corresponding author
Suresh Kumar
SA Jain Institute of Management & Technology,
Ambala City 134003, Haryana, India
E-mail: sureshturka@gmail.com
M. Khurrum S. Bhutta
Department of Management Systems,
College of Business,
Ohio University,
Athens, Ohio, USA
E-mail: bhutta@ohio.edu
Vivek S. Natarajan
Department of Management and Marketing,
Lamar University,
Beaumont, Texas, USA
E-mail: vivek.natarajan@lamar.edu
Abstract: Competing for a perennial supply of milk is a major factor
influencing dairy plants, and theoretically, they must be positioned at an
optimum distance between themselves in order to sustain their profitability.
However, the location optimised on economic variables seldom corresponds
with the actual location of a dairy plant as the final selection is an outcome of a
complex set of variables, both objective and subjective in nature. This paper
models the influence of various subjective and objective factors on location
strategies of the dairy plants in India. Findings indicate that the demographic
factors, represented as population density, employment and literacy emerge as
the most significant influencers of the choice of a manufacturing location of
small-, medium- and large-scale units. Among the subjective variables, the site
specific and micro-factors, comprising of the regulatory framework, and the
site-specific fixed costs score over the macro-factors while selecting a location.
Keywords: dairy industry; location; objective factors; subjective factors.
Reference to this paper should be made as follows: Sharma, T., Kumar, S.,
Bhutta, M.K.S. and Natarajan, V.S. (xxxx) ‘Location strategies of dairy plants
in India’, Int. J. Indian Culture and Business Management, Vol. x, No. x,
pp.xx–xx.
2 T. Sharma et al.
Biographical notes: Tejinder Sharma is a Reader in the Department of
Commerce, Kurukshetra University, Kurukshetra, India. He holds PhD from
Kurukshetra University for ‘Value delivery systems: a study of selected
industries’. His research interests are marketing, business strategy, corporate
social responsibility and business ethics.
Suresh Kumar is a Lecturer in Business Management at SA Jain Institute of
Management & Technology, Ambala City, India. He was awarded his PhD in
2007 for ‘Factors influencing the choice of manufacturing location: a study of
selected industries’. He has research interests in marketing and business
strategy.
M. Khurrum S. Bhutta is currently an Associate Professor of Operations
Management at Ohio University. He received his Doctorate from University of
Texas, Arlington and has published in several leading journals including,
Journal of International Technology and Information Management, Int. J.
Production Economics, Supply Chain Management: An International Journal,
Benchmarking an International Journal, and Int. J. Integrated Supply
Management.
Vivek S. Natarajan is currently an Assistant Professor of Marketing at Lamar
University. He has research interests in the area of marketing strategy,
international business, new product development and healthcare. He received
his Doctorate from University of Texas, Arlington and has published in
Strategic Management Journal, Journal of Business and Economic Research,
and ASBBS E-journal.
1 Introduction
Manufacturing location is a basic element of the competitive business strategy and a vital
aspect of logistics management. The issue of location has its interface with government
policy, and this subject has been studied from the perspective of public administration,
law and even welfare economics (Ballard and Kuhn, 2006; Klaus and Storcken, 2002;
Owen and Daskin, 1998). This issue is important for all the industries, irrespective of
their nature. Be it manufacturing industry, agro-based industry, service outlet, wholesale
or retail store, location is an important consideration not only from the operational
aspects but also from the strategic perspective. Most other variables of a strategy can be
controlled or manipulated, but once a business unit is set up at a particular location, it
cannot be changed in short run.
The dairying industry in north India has undergone a paradigm shift with an increased
supply of milk into the organised sector, leading to a large number of dairy plants being
set up in the last two decades. However, the growth in the milk production has not been
commensurate with the increasing demand of the organised sector and there is intense
competition amongst the dairy plants. Theoretically, there should be an optimum distance
between them in order to sustain their profitability. However, the location optimised on
economic variables seldom corresponds with the actual location of a dairy plant as the
final location is an outcome of a complex set of variables, both objective and subjective
in nature. The article investigates the set of objective and subjective variables that
influence the choice of a manufacturing location of a dairy plant.
Location strategies of dairy plants in India 3
The approaches for identifying the manufacturing location have been categorised as
macrolevel and microlevel approaches. The macrolevel factors, although being generic in
nature, can significantly influence the feasibility of a manufacturing location. Several
theories have been developed to study the macroapproaches to manufacturing location.
Amongst the important theories are the Weber’s (1929) Material Orientation Theory, Von
Thunen’s (1966) Isolated City State Theory, Hoover's (1948) Transfer Cost Theory,
Losch’s (1954) Economics of Location, Evans (1990) Model of Location, Smith (1981)
Spatial Margins Theory, and Product Life Model of Plant Location. The microperspective
examines more specific site selection factors. The microfactors include the infrastructure,
availability of labour, market, access to raw material and components, industrial
agglomerations, regulatory framework, quality of life, population, weather and climate,
etc. In addition to these factors, last but not the least, are the personal preferences of the
entrepreneurs in selecting the best site for business operations is also important.
The relative importance of these factors varies significantly for different industries.
From the theoretical perspective, each of theories of location presents its own lines of
arguments, within its own set of assumptions. However, the dynamics of changing
business environments make it necessary to subject these theoretical constructs to
empirical testing (Daskin, 2008; Shen and Yu, 2009).
2 Literature review
The issue of manufacturing location figures in many disciplines, each analysing it from
its own perspective. In context of business management, the studies concentrate on
identifying one or more factors underlying the choice of a manufacturing location. The
studies can be broadly classified into three categories – studies analysing the impact of
macro-economic and regulatory mechanism on the choice of a manufacturing location,
studies on the influence of the operational inputs on location decisions and the studies
identifying the supporting factors around a manufacturing location.
The process of manufacturing location commences from identifying the macrolevel
influencers and culminates at the site-specific characteristics. The locations with
favourable macroeconomic and regulatory environment attract more investment. Mani
et al. (1997) observe that the existing business activities and the agglomeration
economies are the attractive location factors for any manufacturing location. Industrial
agglomeration has both positive and negative effects on a location and its impact varies
by the industry, the region-specific attributes and the selected measures of economic
activity (Kim et al., 2000). Ulbrich (1994) finds that business property taxes and the
corporate income tax also play a moderate role in the selection of a location amongst the
states in the U.S. While many site-specific factors are quantifiable and commonly used in
the cost–benefit analysis (CBA), but many of the problems in the location search process
are non-quantifiable (Wheeler et al., 1998). In the Indian context, Brathwal (2000) has
observed that the concentration of new firms in big cities can be attributed to the existing
industrial agglomeration. Amongst the regulatory factors, population and pollution
regulation (Gray, 1997) and fiscal policy (McNamara et al., 1988) have a negative impact
on the choice of a manufacturing location. The dairy industry, being a highly regulated
food sector, is also influenced by regulation (Pritchard, 2001). More recently, Luo et al.
(2008) have also observed that well established factors, such as natural resources and low
labour costs, are not important factors for investment locations in China. Instead, policy
4 T. Sharma et al.
incentives and industrial agglomeration of a location are more important factors for
investment decisions.
A manufacturing location is also expected to contribute to the operational competence
and cost reduction. Infrastructure has been a major factor influencing a manufacturing
location. Several studies have been conducted to find its effect at the country level
(Henderson and McNamra, 2000; Rainey and McNamra, 1999; Smith et al., 1978;
Woodward, 1992) and the state level (Bartik, 1985, 1989; Coughlin et al., 1991;
Glickman and Woodward, 1988). A positive influence of the availability of infrastructure
on the manufacturing location has been reported. It is noteworthy that with an increased
investment in the infrastructure countries like India and China have been able to attract a
significant amount of foreign investment. Henderson and McNamra (1997) report the
availability of land, for present and future projects, as another infrastructural component
of a manufacturing location. Kiran and Kaur (2008) acknowledge the significance of
productivity as a key indicator of successful restructuring and up-gradation by firms. The
existence of high-end infrastructure facilities facilitates higher productivity of the firms.
The labour quality (McNamara et al., 1988) and lower labour cost also increase the
attractiveness of an area for manufacturing plants (Kiran and Kaur, 2008; Schemenner
et al., 1987). Access to markets has been found to have a positive effect on the
manufacturing location (Bartik, 1989; Duffy, 1994; Goetz, 1997; Woodward, 1992). The
proximity to inputs (Hanink, 1996) and the availability of skilled labour also favourably
influence a manufacturing location. Atmanand (2004) observes that logistics facilities,
particularly the facility of truck transportation, are considered important over other means
of transport. A location for all sectors of manufacturing industry is expected to contribute
to cost reduction (Lall and Chakravorty, 2004; Misra and Misra, 2007). In the context of
the dairy industry, Varadrajan (1997) supports that a dairy plant should be located in
close proximity to areas rich in milk availability (Vivek et al., 2009). Other important
factors influencing the manufacturing location of the dairy industry are operating costs
(Joshi and Dangayach, 2009; Shah, 2000) and the logistics facilities (Kulkarni, 2005).
In addition to the macrolevel and microlevel factors imparting operational
competence to a manufacturing unit, there are several supporting factors, which can
become important considerations while selecting a location. Dorf and Emerson (1978)
and McNamara (1991) have identified community size, distance from urban areas and
labour force as the major determinants of a location. Another early study (Scehmenner,
1979) observes that the choice of the plant location must be in relationship with other
plants in a multiple plant network. Local community attributes also influence the location
choices of firms (McNamara et al., 2003). Other factors that influence the choice of a
manufacturing location are the quality of life, including the availability of educational
institutions (Smith et al., 1978), recreational characteristics and the weather and climatic
conditions (Hanink, 1996). In the case of small firms, Mazzarol and Choo (2003) have
found subjective decision making, comprising personal factors such as proximity of site
to home, etc., as greater influencers of manufacturing location, over the objective factors
like market proximity or logistics facilities, etc.
3 Research problem and objectives
The research literature shows that location decision is a function of a multitude of factors,
ranging from macrolevel environmental and economic factors to site-specific
Location strategies of dairy plants in India 5
microfactors. Location decisions are also subject to the psychographic/subjective
preferences of the decision makers. The relative importance of these factors may vary and
each of these factors may constrain or facilitate the choice of a location. Therefore, there
is a need to study the mix of various objective and subjective factors that influence the
manufacturing location. Specifically, the objectives of research are as follows:
1 To identify sets of various objective and subjective factors that influence the
manufacturing location of the firms in the dairy industry.
2 To ascertain the degree of relative significance of various factors that influence a
manufacturing location by way of developing a model linking location decision and
their various predictor factors.
4 Research methodology
4.1 Data collection
Data were collected from both primary and secondary sources. In order to collect the
primary data, a questionnaire was developed and administered personally to the
respondents. The secondary data were collected from various libraries, including
Kurukshetra and Panjab University libraries, National Dairy Research Institute Library,
MDI Gurgaon Library, indexing services of Vikram Sarabhai Library at IIM Ahmedabad,
etc. The important government publications referred to include Economic Survey,
Statistical Abstracts and databases of Indian Dairy Association, Ministry of Food and
Civil Supplies.
4.2 Sampling
As per the records of the Directorate of Industries, Government of Haryana and the
directory of the dairy industry, published by the Indian Dairy Association, there are
25 dairy units in the organised sector. All the units were chosen for study. Five dairy
units are located in Punjab state, but in close proximity with Haryana (Ambala-
Chandigarh Highway). They directly compete with the dairy units in Haryana and their
effect could not be ignored. Therefore, they are also included in the study. The final
sample size of the dairy units was 30, of which, five are in the cooperative sector and the
rest were in the private sector.
4.3 Questionnaire
Primary data on the subjective variables influencing the choice of a dairy location were
collected by means of a questionnaire, administered personally to the top managers of the
dairy units. The questionnaire was divided into three sections. The first section contained
the questions eliciting general information about the firm. The second section, entitled
‘factual aspects of manufacturing location’, comprised questions related to cost aspects,
raw material collection, transportation, market profile, etc. The responses were taken in
the form of absolute data and the binomial/nominal scale. The third section comprised of
6 T. Sharma et al.
the questions regarding the subjective aspects of manufacturing location. A set of 15
variables was identified as the factors influencing the choice of a dairy plant. These
include raw material availability and characteristics, competition, operating costs, human
resource issues, utilities, demographic characteristics, infrastructure, fixed cost, market,
transportation cost, tax structure, technology, pollution, regulatory environment and
microenvironment. In order to collect more reliable information, these variables were
broken into 58 question items and the respondents were asked to record their opinion on
the relative importance of these factors on a five-point interval scale. After the data
collection, index scores for the 15 variables were developed by the summation of the
scores of the questions pertaining to the question items. These index scores were taken
for the subsequent analysis.
4.4 Statistical analysis
In order to develop theoretical models of these sets of factors determining the choice of a
manufacturing location, regression models, the most commonly used econometric
modelling technique, have been used. These are expressed in the form of the following
mathematical expressions:
λ = f (
θ
, σ)
λ
θ
= f (V
θ
1
, V
θ
2
, …, V
θ
n
)
λ
σ
= f (V
σ
1
, V
σ
2
, …, V
σ
n
)
• where λ = predictor of a manufacturing location
•
θ
= objective factors influencing manufacturing location
•
σ
= subjective factors influencing manufacturing location
• V
θ
i
= objective variables influencing manufacturing location
• V
σ
i
= subjective variables influencing manufacturing location.
4.4.1 Modelling objective variables
The common set of objective variables influencing the manufacturing location are the
population, sex ratio, education, employment, labour characteristics, banking
development, total area, per capita income, information technology and communication,
road, rail and air transport, credit deposited into banks, crime rate, weather and climate.
Of these variables, we selected the eight variables, i.e. area, population density, number
of literate people, total employment, number of banks, credit deposits, metal led roads
and number of vehicles in different districts of the state of Haryana. The predictor
variables chosen for the study are the number of units, and the quantum of investment in
the districts. Since, the small-scale units (with an investment up to Rs. 5 million in plant
and machinery) far out-number the medium (investment up to Rs. 50 million) and large
units (investment up to Rs. 500 million), separate models have been developed for these
categories.
Location strategies of dairy plants in India 7
4.4.2 Modelling of subjective variables
The index scores of the subjective variables, as explained in Section 4.3, were taken as
independent variables of the subjective data, and the distance from the nearest competitor
was taken as the dependent variable for choosing a manufacturing location of the dairy
plant. In order to study the linkages of the subjective variable, the regression modelling
was done using backward remove method. Under this method, the regression model is
first developed by taking all the predictor variables. Thereafter, the variables with the
least significant t-value are removed and the regression is run again. The process is
continued until all the predictors are statistically significant. This method allows each
variable to be represented before a final model is developed.
5 Findings
5.1 Models of objective variables
Different models linking the eight objective variables, as explained earlier, with the
chosen indicator of manufacturing location were developed. In the models M
IS
and M
ILM
,
the total investment in small units; and medium and large units, respectively, were taken
as the dependent variable. In the models M
NS
and M
NLM
, the number of small units and
number of medium and large units, respectively, were taken as the dependent variables.
The findings of the regression analysis are shown in Tables 1 and 2:
The below table shows the values of coefficient of determination (r
2
) and their
corresponding F-values of the four models, which are statistically significant at 95%
level of confidence. Multi-collinearity diagnostics was conducted to ensure that there is
no correlation between the predictor variables. The beta values, t-values and level of
significance of the predictor variables are shown in Table 2.
As shown in Table 2, population density has emerged as the most important variable
influencing the investment in small scale industries (based on the Beta and t-values), and
medium and large units (Models M
IS
, M
ILM
). It may be concluded that districts with
larger population density attract higher investment, analogous to the dart-board model of
manufacturing location. The small enterprises usually rely on the workforce available in
the vicinity of the unit and also market their products to the nearby areas. They employ
less sophisticated technologies with relatively higher inputs for manual labour. Since
higher population is likely to have higher number of workers as well, this has emerged as
a significant factor in attracting investment.
Table 1 Coefficient of determination and F-value of different models
Model Coefficient of determination (R
2
) F-value Significance
(M
IS
) 0.939 19.279 0.000
(M
ILM
) 0.765 4.079 0.021
(M
NS
) 0.951 24.278 0.000
(M
NLM
) 0.941 19.950 0.000
8 T. Sharma et al.
Table 2 Beta value, t-value and level of significance of independent variables
Variable name M
IS
M
ILM
M
NS
M
NLM
B 0.242 0.733 –0.143 0.425
T 0.791 1.220 –0.522 1.411
Total area
Sig 0.447 0.250 0.613 0.189
B 0.881 1.988 0.166 0.556
T 3.100 3.565 0.652 1.988
Population
Sig 0.011 0.005 0.529 0.075
B 0.078 –0.613 0.482 0.256
T 0.327 –1.307 2.251 1.091
Literacy
Sig 0.750 0.220 0.048 0.301
B 0.339 0.518 0.340 0.931
T 1.886 1.468 2.108 5.264
Total
employment
Sig 0.089 0.173 0.061 0.000
B –0.066 –0.173 0.029 –0.235
T –0.256 –0.341 0.125 –0.927
Scheduled
commercial
banks
Sig 0.803 0.740 0.903 0.376
B –0.029 0.272 0.064 –0.091
T –0.307 1.450 0.750 –0.970
Deposits in
banks
Sig 0.765 0.178 0.470 0.355
B 0.253 0.160 0.367 –0.166
T 0.992 0.320 1.600 –0.661
Metalled road
Sig 0.344 0.756 0.140 0.524
B 0.004 –0.701 0.140 –0.136
T 0.010 –0.958 0.138 –0.370
No. of
registered
vehicles
Sig 0.992 0.361 0.413 0.719
In the third (M
NS
) and fourth (M
NLM
) models, literacy and total employment are the
statistically significant predictor variables influencing the number of small scale
industries and medium and large units, respectively. With the increasing education level
of the younger strata of population, the entrepreneurs are trying to improve their
technologies and prefer literate workforce. Therefore, higher level of literacy and
working population emerge as significant factors in attracting the manufacturing units.
5.2 Models of subjective variables
Nine different models emerged as a consequence of removal of the variables with the
lowest level of significance. The first model (M
Si
) had 15 predictor variables, which were
finally reduced to seven predictor variables (significant at 95% level of confidence).
Multi-collinearity diagnostics was performed to ascertain that the independent variables
were not inter-related. The results of the analysis are shown in Tables 3–5.
Location strategies of dairy plants in India 9
The coefficient of determination and the F-value of the nine emerging models are
shown in Table 3. In all the models, the r
2
values range from 0.767 to 0.863. Barring the
first (M
Si
) and second model (M
Sii
), the F-values and the r
2
are significant at 95% levels
of confidence. Table 4 shows the beta value, t-value, and the level of significance of each
of the variables in the nine models while Table 5 shows these values for the removed
subjective predictor variables in various models.
In the first model (M
Si
), regulatory environment, microenvironment, pollution,
demographic characteristics, and operating cost of a location are the significant predictors
while other listed predictor variables are not statistically significant. The variable human
resource issues have a p-value of 0.885, which is insignificant and is removed in the next
model. In the second model (M
Sii
) also, the most significant predictor variables are the
regulatory environment, microenvironment, pollution, demographic characteristics,
operating cost, and fixed cost. In this model, competition is found to have the lowest
p-value and is removed. In the third model (M
Siii
), in addition to the variables significant
in the second model, the regulatory environment, microenvironment, pollution,
demographic characteristics, operating cost and fixed cost, technology also emerge to be
among the significant variables. Since, raw material availability has the lowest level of
significance; it is removed in the subsequent analysis. In the subsequent models, the
significant predictor variables are the regulatory environment, microenvironment,
pollution, demographic characteristics, operating cost, fixed cost and technology. The rest
of the predictor variables are removed from the analysis.
Table 3 Coefficient of determination and F-values of different models
Model R R
2
F-value Sig.
M
Si
0.929 0.863 2.515 0.131
M
Sii
0.929 0.862 3.129 0.067
M
Siii
0.928 0.861 3.799 0.033
M
Siv
0.919 0.845 4.074 0.021
M
Sv
0.914 0.835 4.614 0.011
M
Svi
0.908 0.824 5.146 0.006
M
Svii
0.897 0.805 5.507 0.004
M
Sviii
0.887 0.787 6.013 0.002
M
Six
0.876 0.767 6.573 0.001
10 T. Sharma et al.
Table 4 t-Value and level of significance of subjective predictors in various models
t
–
–
–2.550
–
–
4.160
–
1.893
–
–
–
–2.196
–3.260
4.881
–6.076
MS ix
Beta
–
–
–0.580*
–
–
0.730*
–
0.356
–
–
–
–0.373*
–0.497*
1.306*
–1.321*
t
–
–
–2.577
–
–1.121
4.200
–
2.201
–
–
–
–2.483
–3.413
5.051
–6.215
MS viii
Beta
–
–
–0.581*
–
–0.186*
0.731*
–
0.449*
–
–
–
–0.464*
–0.521*
1.369*
–1.346*
t
–
–
–2.398
–
–1.276
4.309
–
2.420
–1.047
–
–
–2.696
–3.533
5.155
–6.226
MS vii
Beta
–
–
–0.545
–
–0.214
0.801*
–
0.591*
–0.243
–
–
–0.564*
–0.542*
1.454*
–1.344*
t
–
–
–2.600
–
–1.299
4.464
–
2.663
–1.491
1.084
–
–2.920
–3.596
5.184
–6.077
MS vi
Beta
–
–
–0.602*
–
–0.216
0.866*
–
0.690*
–0.424
0.317
–
–0.667*
–0.644*
1.452*
–1.463*
t
–
–
–2.671
–
1.212
4.280
0.837
2.291
–1.642
1.288
–
–2.588
–3.643
4.933
–5.751
MS v
Beta
–
–
0.637
–
–0.205
0.847
0.215
0.626*
–0.492
0.405
–
–0.618*
–0.684*
1.416
–1.563*
t
–
–
–2.604
–
–1.385
3.896
1.089
2.309
–1.367
1.439
–0.727
–2.102
–3.550
4.520
–5.607
MS iv
Beta
–
–
0.745*
–
–0.272
0.972*
0.358
0.652*
–0.434
0.501
–0.236
–0.550
–0.744*
1.366*
–1.561*
t
–0.960
–
–2.706
–
–1.679
3.365
1.216
2.485
–1.303
1.606
–1.088
–2.295
–3.638
4.595
–5.619
MS iii
Beta
–0.221
–
–0.919*
–
–0.415
1.189*
0.406
0.743
–0.416
0.575
–0.402
–0.642
–0.826*
1.445*
–1.643*
t
–0.622
–0.289
–2.423
–
–1.476
2.843
1.180
1.945
–1.141
1.369
–0.894
–1.659
–3.249
4.244
–5.284
MS ii
Beta
–0.178
–0.064
–0.896*
–
–0.398
1.147*
0.432
0.696
–0.396
0.543
–0.367
–0.586
–0.808*
1.431*
–1.642
t
–0.592
–0.221
–2.247
0.151
–1.375
2.637
0.876
1.781
–1.039
1.269
–0.843
–1.539
–3.006
3.886
–4.837
MS i
Beta
–0.197
–0.055
–0.895
0.038
–0.405
1.154*
0.398
0.690
–0.390
0.559
–0.379
–0.598
–0.807*
1.443*
–1.635*
Variables
Raw material
Competition
Operating
cost
Human
resource
issues
Utilities
Demographic
characteristics
Infrastructure
Fixed cost
Market
Transportation
cost
Tax structure
Technology
Pollution
Regulatory
environment
Micro
environment
*Significant at 95% level of confidence.
Location strategies of dairy plants in India 11
Table 5 B-value, t-value and level of significance of subjective predictors removed in
the emerging models
Unstandardised
coefficients
Standardised
coefficients
Model
B Std. error Beta
t Sig.
M
Sii
Human Resource Issues 0.038 0.151 0.885 0.062 0.370
M
Siii
Human Resource Issues
Competition
0.052
–0.064
0.230
–0.289
0.824
0.781
0.087
–0.109
0.395
0.401
M
Siv
Human Resource Issues
Competition
Raw Material Availability
–0.009
–0.136
–0.221
–0.041
–0.745
–0.960
0.968
0.478
0.365
–0.015
–0.255
–0.321
0.432
0.549
0.328
M
Sv
Human Resource Issues
Competition
Raw Material Availability
Tax Structure
–0.002
–0.138
–0.104
–0.236
–0.01
–0.784
–0.506
–0.727
0.991
0.453
0.625
0.486
–0.004
–0.253
–0.166
–0.235
0.433
0.549
0.419
0.163
M
Svi
Human Resource
Competition
Raw Material Availability
Tax Structure
Infrastructure
0.072
–0.071
–0.130
–0.025
0.215
0.426
–0.429
–0.658
–0.094
0.837
0.679
0.677
0.525
0.927
0.422
0.133
–0.134
–0.204
–0.030
0.256
0.605
0.633
0.433
0.254
0.249
M
Svii
Human Resource Issues
Competition
Raw Material Availability
Tax Structure
Infrastructure
Transportation Cost
0.016
–0.110
–0.112
0.012
0.104
0.317
0.100
–0.695
–0.562
0.048
0.417
1.087
0.922
0.501
0.585
0.963
0.685
0.301
0.030
–0.205
–0.167
0.014
0.125
0.311
0.661
0.680
0.436
0.259
0.280
0.188
6 Discussion
Results show that amongst the objective variables influencing the manufacturing
location, the demographic variables, comprising population density, employment and
literacy, emerge as the most significant variables associated with choosing a
manufacturing location of the industries (small, medium and large), in terms of both
number and investment. This supports Dorf and Emerson’s (1978) findings that
community size has a positive influence on the choice of a site as a manufacturing
location. It may be noted that Haryana has well developed transportation, and other
infrastructure that can influence the choice of manufacturing location. Therefore, the
demographic variables seem to score over the other variables. The population serves the
twin purpose of being an input to the manufacturing operations as well as the market for
the consumption of manufactured output. This finding supports the dart-board theory as
larger area can imply larger population and hence better profitability from a location.
12 T. Sharma et al.
Amongst the subjective variables, the regulatory environment has emerged as the
most significant factor influencing the choice of a manufacturing location, in line with the
previous findings of Gray (1997), McNamara et al. (1988), Pritchard (2001) and Luo
et al. (2008). The prevailing government rules and regulations are a major determinant of
any dairy plant location. Since its beginning, the dairy industry has been subjected to
immense government control. Despite the liberalisation measures of 1991, India’s dairy
industry continues to be under strict government regulations because of social reasons.
The output of the dairy industry is highly perishable and its quality can have serious
health implications for the society. Therefore, the government keeps strict control of the
dairy units. Similarly, raw material procurement is also associated with its social
implication in terms of providing remunerative prices to its farmers. Therefore, any dairy
entrepreneur would first consider the regulatory aspects of any manufacturing location.
The second most important variable influencing the choice of a dairy location is the
demographic characteristics, which support the earlier model emerging out of the
objective variables. Population density is also overtly associated with business
infrastructure and quality of life; therefore, a location with higher population density
around it would always be considered by the entrepreneurs. The third important variable
influencing dairy plant location is the fixed cost. Interestingly, earlier literature focuses
on the operating cost components, such as raw material, labour cost, logistics costs,
operating costs, etc., as the major influencers of a manufacturing location. The possible
difference in the findings could be attributed to an unprecedented increase in the prices of
the real estate, construction inputs, machinery, etc, leading to significant increase in the
fixed cost.
The variables microenvironment, pollution, operating cost and technology have a
negative influence on choosing the manufacturing location. Microenvironment includes
local law and order situation, administrative policies, crime and climatic conditions of a
location. If these conditions are not favourable, then any entrepreneur would not like to
locate industry in the area. The presence of terrorism and corruption also has a negative
impact on dairy plants location. Pollution is a major problem of dairy plants. The strict
implementation of pollution rules of the government discourages the entrepreneurs from
locating the industry in that area. Operating cost has a negative influence on dairy plant’s
location. In dairy plants, a high proportion of total cost is expended on operating
activities. Higher wages and salaries, high processing cost, costly services, and utilities
also discourage the entrepreneurs when locating a dairy unit. Technological advancement
in dairy processing minimises the processing cost but increase the fixed cost of plant and
machinery. Hence, the higher cost of latest technology negatively influences the dairy
plants location.
7 Conclusions and future research
It can be concluded from the earlier discussion that the site-specific and microfactors
score over the macrofactors while choosing the location of a dairy plant. There is a high
concentration of industrial units in the districts with high population density. The
subjective variables also support the findings of the objective variables as demographic
characteristics of the site are an important consideration. The microfactors also emerge as
important consideration as entrepreneurs feel they have a greater influence on day to day
functioning of the units. For the policy makers, it is recommended that in addition to
Location strategies of dairy plants in India 13
investments in infrastructure, they must concentrate on creating a congenial regulatory
frame work and improving upon the microfactor, including law and order, corruption, etc.
The research on location is a never-ending phenomenon, and for the researchers
aspiring for further research on the area, research opportunities exist on developing the
newer theories of location and investigating empirically, whether proper location
maximises the profits of the business firms. The models developed in the study may be
validated by selecting other research designs and statistical techniques such as structured
equation modelling. The models could be studied on other industries as well. In the
rapidly changing industrial scenario, the role of government has been increased
tremendously. The establishment of special economic zones is emerging as the preferred
location for setting up the manufacturing units. The study must be conducted to include
the government incentives and support, which are an important determinant of the
location.
Annexure
FACTORS INFLUENCING LOCATION OF DAIRY PLANTS
QUESTIONNAIRE
Section-I
General Information
1 Name and Address of the Firm ____________________
____________________
____________________
2 Year of Establishment ____________________
3 What Products do you manufacture and in what proportion?
Product categories % of output
(i) _________________ _________________
(ii) _________________ _________________
(iii) _________________ _________________
(iv) _________________ _________________
(v) _________________ _________________
4 (a) What is the installed capacity of your plant?
_________ (thousand litres/day)
5 (a) Where are the following departments of your plant located?
(i) Registered office _________________
(ii) Marketing office _________________
(iii) Head office _________________
14 T. Sharma et al.
(b) How far are the following from you plant?
(i) Nearest railway station _________________ kms
(ii) Nearest national highway _________________ kms
(iii) Nearest state highway _________________ kms
(iv) Nearest link road _________________ kms
6 (a) What is the area of your plant in acres? ___________
(b) Which other dairy plants are located near your area?.
Name of Plant Smallest Distance in kms
(i) _________________ _________________
(ii) _________________ _________________
(iii) _________________ _________________
(iv) _________________ _________________
7 Have you availed the following facilities from the government?
(i) Subsidised land ( )
(ii) Roads ( )
(iii) Communication ( )
(iv) Subsidised Electricity ( )
(v) Tax Holidays ( )
(vi) Any Other ( )
Section-II
Factual Aspects of Manufacturing Location
8 What are various components of cost of finished products?
Cost component % contribution
(i) Raw material cost ________________
(ii) Processing cost ________________
(iii) Transportation cost of raw material ________________
(iv) Transportation cost of finished goods ________________
(v) Taxes ________________
(vi) Any other ________________
Location strategies of dairy plants in India 15
9 Please specify the quantity of milk collected within the following areas?
% of milk
(i) Up to 50 kms ________________
(ii) 50–150 kms ________________
(iii) 150–300 kms ________________
(iv) More than 300 kms ________________
10 How many trucks are used to bring milk to your plant?
No:
(i) Small vehicles (up to 1000 litre) ___________
(ii) Light weight small vehicles (up to 5000 litre) ___________
(iii) Tankers (10000–12000 litre) ___________
(iv) Any other ___________
11 Cost of transportation is;
Rs. Per km./per day
(i) Company __________________
(ii) Producer Owned __________________
(iii) Hired Vehicles __________________
(iv) Other vehicles __________________
12 Please specify the %age of sale within the following areas.
% of sale
(i) Up to 100 kms __________________
(ii) 100–300 kms. __________________
(iii) 300–500 kms __________________
(iv) More than 500 kms __________________
(v) Other bulk market __________________
Section-III
Subjective Aspects of Manufacturing Location
13 How developed are the following facilities around your plant?
Please score as under:
Well developed (5), Moderately developed (4), Satisfactory (3), Unsatisfactory (2),
Poor (1)
16 T. Sharma et al.
(i) Labour ( )
Unskilled ( )
Trained ( )
Qualified ( )
Managerial ( )
(ii) Raw material ( )
(iii) Water availability ( )
(iv) Transport ( )
(v) Electricity ( )
(vi) Boiler Fuel ( )
(vii) Banking Vehicle fuel and petrol ( )
(viii) Communication ( )
(ix) Housing ( )
(x) Living conditions ( )
(xi) Education ( )
(xii) Health of community ( )
(xiii) Park ( )
14 Please specify the extent to which the following factors influence the choice of dairy
location?
Very Significant (VS), Significant (S), Neutral (N), Insignificant (I)
Very Insignificant (VI)
VS S N I VI
• Near-by availability of milk.
( ) ( ) ( ) ( ) ( )
• Quantity of milk purchased by
unorganised sector
( ) ( ) ( ) ( ) ( )
• Price of milk paid by unorganised sector
( ) ( ) ( ) ( ) ( )
• Price of milk paid by other companies
( ) ( ) ( ) ( ) ( )
• Processing cost of milk
( ) ( ) ( ) ( ) ( )
• Quality of milk
( ) ( ) ( ) ( ) ( )
• Availability of labour
( ) ( ) ( ) ( ) ( )
• Work culture of labour
( ) ( ) ( ) ( ) ( )
• Customs and beliefs of labour
( ) ( ) ( ) ( ) ( )
• Local holidays & festivals
( ) ( ) ( ) ( ) ( )
• Availability of water
( ) ( ) ( ) ( ) ( )
• Availability of land
( ) ( ) ( ) ( ) ( )
Location strategies of dairy plants in India 17
• Availability of capital
( ) ( ) ( ) ( ) ( )
• Cost of capital
( ) ( ) ( ) ( ) ( )
• Banking facilities
( ) ( ) ( ) ( ) ( )
• Easy approachability of plant
( ) ( ) ( ) ( ) ( )
• Cost of construction of plant
( ) ( ) ( ) ( ) ( )
• Cost of fuel & power
( ) ( ) ( ) ( ) ( )
• Wages and salaries paid to staff
( ) ( ) ( ) ( ) ( )
• Market area
( ) ( ) ( ) ( ) ( )
• Electricity
( ) ( ) ( ) ( ) ( )
• Rate of insurance
( ) ( ) ( ) ( ) ( )
• Transportation cost of milk
( ) ( ) ( ) ( ) ( )
• Transportation cost of finished goods
( ) ( ) ( ) ( ) ( )
• Existing plants
( ) ( ) ( ) ( ) ( )
• Route selection
( ) ( ) ( ) ( ) ( )
• Road tax payments
( ) ( ) ( ) ( ) ( )
• Toll Tax
( ) ( ) ( ) ( ) ( )
• Octroi policies of state
( ) ( ) ( ) ( ) ( )
• Excise duty
( ) ( ) ( ) ( ) ( )
• Sales tax
( ) ( ) ( ) ( ) ( )
• Other taxes
( ) ( ) ( ) ( ) ( )
• Demand for milk products
( ) ( ) ( ) ( ) ( )
• Potential for expansion
( ) ( ) ( ) ( ) ( )
• Export facilities
( ) ( ) ( ) ( ) ( )
• Co-operative movement for milk
collection
( ) ( ) ( ) ( ) ( )
• Population density
( ) ( ) ( ) ( ) ( )
• Per capital income of the people
( ) ( ) ( ) ( ) ( )
• Economic development of the region
( ) ( ) ( ) ( ) ( )
• Information technology and
computerisation
( ) ( ) ( ) ( ) ( )
• Technological superior modes of
transportation
( ) ( ) ( ) ( ) ( )
• Technology of milk processing
( ) ( ) ( ) ( ) ( )
• Environmental permits
( ) ( ) ( ) ( ) ( )
• Water pollution level of the plant
( ) ( ) ( ) ( ) ( )
• Pollution rules of the government
( ) ( ) ( ) ( ) ( )
• Political condition of the state
( ) ( ) ( ) ( ) ( )
• Law and order situation
( ) ( ) ( ) ( ) ( )
• Inspector raj
( ) ( ) ( ) ( ) ( )
18 T. Sharma et al.
• Interference by local authorities
( ) ( ) ( ) ( ) ( )
• Risk of fire/theft etc.
( ) ( ) ( ) ( ) ( )
• Earth quake proneness of your location
( ) ( ) ( ) ( ) ( )
• Weather and climate of the location
( ) ( ) ( ) ( ) ( )
• De-licensing of dairy industry
( ) ( ) ( ) ( ) ( )
• Central government policies.
( ) ( ) ( ) ( ) ( )
• State government policies related to
industries.
( ) ( ) ( ) ( ) ( )
• Manufacturing location subsides
( ) ( ) ( ) ( ) ( )
Acknowledgements
The authors thankfully acknowledge the helpful comments of Prof. Angappa
Gunasekaran, Dr. Satya Parayitam and the anonymous reviewers.
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