An Analytical Study on Ratios Influencing Profitability of Selected Indian Automobile Players

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-12, Issue-3 (June 2022)
www.ijemr.net https://doi.org/10.31033/ijemr.12.3.14
102 This work is licensed under Creative Commons Attribution 4.0 International License.
An Analytical Study on Ratios Influencing Profitability of Selected
Indian Automobile Players
M.Elangovan1
and Dr. K.Ramasamy2
1
Research Scholar (Commerce), Sri Krishna Arts and Science College Coimbatore, INDIA
2
Assistant Professor, Department of Management Sciences, Sri Krishna Arts and Science College Coimbatore, INDIA
1
Corresponding Author: elangovanyadav@gmail.com
ABSTRACT
Every country with a well-developed
transportation network has a well-developed economy. The
automobile industry is a critical engine of the nation's
economic development. The automobile industry has
significant backward and forward links with every area of
the economy, as well as a strong and progressive multiplier
impact. The automotive industry and the auto component
industry are both included in the vehicle industry. It
includes passenger waggons, light, medium, and heavy
commercial vehicles, as well as multi-utility vehicles such as
jeeps, three-wheelers, military vehicles, motorcycles,
tractors, and auto-components such as engine parts,
batteries, drive transmission parts, electrical, suspension
and chassis parts, and body and other parts. In the last
several years, India's automobile sector has seen incredible
growth in sales, production, innovation, and exports.
India's car industry has emerged as one of the best in the
world, and the auto-ancillary sector is poised to assist the
vehicle sector's expansion. Vehicle manufacturers and
auto-parts manufacturers account for a significant
component of global motorised manufacturing. Vehicle
manufacturers from across the world are keeping a close
eye on the Indian auto sector in order to assess future
demand and establish India as a global manufacturing
base. The current research focuses on three automotive
behemoths: TATA Motors, MRF, and Mahindra &
Mahindra.
Keywords-- Transportation Network, Multi-Utility
Vehicles, Jeeps, Three-Wheelers, Military Vehicles,
Motorcycles, Tractors, TATA Motors, MRF, Mahindra
& Mahindra
I. INTRODUCTION
The first automobile was introduced to Indian
roads in 1897 on the streets of Mumbai (Bombay). Due
to a lack of manufacturing capacity in India, vehicles
were directly imported in tiny quantities from other
countries until 1930. In 1928, American General Motors
formed a fully owned national subsidiary in Bombay to
build trucks (CKD) and cars, and in 1930-31, Canadian
Ford Motors constructed assembly operations in
Mumbai, Calcutta, and Chennai Madras. Hindustan
Motors (HM) was established in 1942 to manufacture
automobile accessories, with their first vehicle being
delivered in 1949. Premier Automobiles Ltd was
founded in 1944, and by 1947, it had produced the first
vehicle in India. At the time of independence, both PAL
and HM concentrated on passenger automobiles,
whereas Mahindra & Mahindra was founded in 1945
with the goal of producing utility vehicles. Under a
licence from Piaggio, the Bombay authorities approved a
scheme by Bajaj Auto to replace the rickshaw-cycle with
an automobile in 1947. On the suggestion of the first
Tariff Commission, the two regimes ordered that only
firms with a manufacturing programme be allowed to
operate in India, excluding organisations that just
assemble foreign-made components and those with no
Indian partner. Due to nationalisation and the licencing
raj, as well as trade limitations on imports that impeded
the Indian private sector, overall industrial development
was very moderate throughout the 1950s and 1960s.
Between the 1970s and 1980s, the automotive sector
began to expand, and demand for tractors, commercial
vehicles, and scooters increased, albeit to a lesser
amount. Only three vehicle manufacturers, Premier
Automobiles ltd, Hindustan Motors ltd, and Standard
Motors ltd, held entire control of the industry until 1982.
The administration allowed foreign innovation and
technology to be shared with Indian organisations with
or without equitable involvement. With the help of the
Indian government, Maruti Udyog Ltd formed a joint
venture with Suzuki in 1982 to begin mass manufacture
of a variety of new compact vehicle models. In the two-
wheeler category, Honda, Kawasaki, Suzuki, and
Yamaha are all from Piaggio (Italy) Japan, Honda, and
for Zundapp Scooters, Steyr Daimler Puch (ompani),
(Germany), and for Mopeds, Agrati Garelli (Italy). The
Indian car business has had consistent expansion since
the establishment of the new Industrial Policy in 1991,
thanks to the removal of restrictions and prohibitions.
The License Raj was abolished, which aided the growth
of the vehicle industry. In 1997, the automobile industry
was buoyed by the government's announcement of a
National Highway Policy. Several Indian automakers,
including Tata Motors ltd, Maruti Suzuki ltd, and M &
M ltd, have expanded their national and international
operations. In the year 2000, the Indian government
introduced mandatory emission standards in order to
safeguard the environment or reduce pollution caused by
cars. The 'Bharat Stage' was the name given to these
courses of action, which were based on strict European
criteria. Bharat-Stage (IV) is now being implemented in
thirteen metropolitan communities: Delhi, Ahmedabad,

Pune, Mumbai, Kolkata, Surat, Kanpur, Chennai,
Bangalore, Hyderabad, Solapur, and Lucknow, Agra,
with the rest of the nation still operating under Bharat-
Stage (III) (III). To stimulate domestic manufacture, the
Indian government charges a 125 percent import tariff on
electric automobiles, while components such as
gearboxes and airbags, as well as drive axle parts, are
taxed at 10%. In this manner, the levies encourage car
manufacturers to construct vehicles in India rather than
importing them as fully completed components or
vehicles.
II. REVIEW OF LITERATURE
This research by Mahipat Ranawat and Rajnish
Tiwari (2009) attempts to identify policies important to
the growth of the Indian motorised sector and also
investigates the regime's efforts to expand the business.
The development of the Indian locomotive sector can be
divided into four phases: first, from 1947 to 1965;
second, from 1966 to 1979, guidelines related to sector
protection, indigenization, and regulation; third, from
1980 to 1990, liberalisation of technology acquisition;
and fourth, from 1991 onwards, liberalisation of direct
foreign investment. By supporting or executing
government policies and strategies, the Indian car
industry emerges as stronger and more progressive.
In their study "The auto Sector in and Beyond
the Crises," David Haugh, Annabelle Mourougane, and
Olivier Chatal (2010) looked at the issues or crisis faced
by the automobile industry, which has been most
damaged by the recession. There has been a significant
drop in automobile demand and sales. This research
examines the automotive sector's position or relevance in
the present cycle, as well as short- and medium-term
forecasts, as well as policy initiatives, such as the vehicle
scrapping programme and the implementation of policies
to promote the automobile industry.
The major goal of this investigation, according
to Vijayakumar (2011) in his work "The Determinants of
Profitability: An Empirical Investigation Using Indian
Automobile Industry," is to examine the aspects of
profitability in the selected Automobile Industry. For the
purpose of study, the automobile industry was divided
into three sections: "Business Vehicles, Passenger Cars
and Multiutility Vehicles, and Two and Three
Wheelers." Following a screening process, 18 firms were
chosen, with 5 in the commercial vehicle section, 4 in
the passenger car and multiutility vehicle segment, and 9
in the two and three wheeler segment. The secondary
data covers the years 1991-1992 through 2003-04. The
findings show that size is the most important indicator of
profitability in the Indian automobile industry, followed
by prior profitability, asset growth rate, vertical
integration, and stock turnover ratio. The research
concluded that industry should investigate all of these
potential aspects or variables in depth when assessing a
company's profitability.
In their article "Impact of Liquidity Ratio on
Profitability: An Empirical Study of the Automobile
Sector in Karachi," Anzala Noor and Samreen Lodhi
(2015) investigate the short-term financial strength of the
automobile industry using financial ratios and the impact
of liquidity ratio on company profitability. The
information was gathered from five auto companies
listed on the Karachi Stock Exchange, namely Nissan
Ghandhara, Toyota, Pak Suzuki, Honda, and Hino Pak,
who gathered information from various sources such as
annual financial statements, reports, journal links, and
company websites for the previous five years (2010-
2014). For regression and significance testing, many
descriptive, correlation, and ANOVA methods are used.
They use return on asset as an independent variable for
the liquidity ratio and return on equity as a dependent
variable for the profitability ratio, ratio, and current
quick ratio as the outcomes to show that profitability and
liquidity have a negative relationship.
III. SAMPLING DEFINITION
For conducting the present study the researcher
has selected three top players in automobile industry of
India including TATA Motors,HeroMotor Corporation
and Mahindra and Mahindra. The financial data for the
same is extracted from CAPITALINE and CMIE for the
period of 10 years from 2011-12 to 2020-21 and the
relevant statistical tools have been applied
IV. MULTIPLE REGRESSION
ANALYSIS
Multiple Regression Analysis is a statistical
method for predicting dependent variables by combining
numerous independent variables. It's a functional
connection between a dependent variable and many
independent factors in which the impact of the
independent variables on the dependent variables
(profitability) is determined via analysis. The present
research used this technique to seek for a new
combination of factors that may explain the differences
in profitability. In the chosen car industry organisations,
Multiple Regression is used using Return on Assets as
the dependent variable and all other factors as
independent variables. Multiple regression analysis is
utilised in this research to determine the link between
variables and the factor impacting profitability.

Table 1: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – Tata Motors
S.No. Ratio of
Multiple
Regression
Co-efficient
t’ value p-value
X1 Quick ratio
.372 3.130 .014**
X2 Interest Coverage Ratio
-.049 -.458 .661
X3 Net working capital to sales
.400 3.852 .005**
X4 Raw material turnover ratio
.162 1.096 .309
X5 Debtors turnover ratio
.082 1.006 .348
X6 Creditors turnover ratio
-.179 -2.228 .061
X7 Distribution expenses ratio
.146 1.562 .162
X8 Miscellaneous expenditure ratio
-.127 -1.693 .134
X9 Operating expenses ratio
.119 1.357 .217
X10 Net fixed assets turnover ratio
-.523 -5.570 .001*
X11 Debt to equity ratio
-.061 -.453 .664
X12 Inventory turnover ratio
-.264 -.623 .553
**significant at 5% level. * Significant at 1% level
ANOVA
Sum of
Squares
df Mean Square F P S/NS
Regression 4.872 3 1.624 56.093 .000 S
Residual .232 8 .029
Total 5.104 11
**significant at 5% level. * Significant at 1% level S- significant NS – Not significant
The table highlights that, the multiple
regression co-efficient values of Tata Motors . It projects
that two variables are individually contribute
significantly to variations in the current ratio when
influence of other variables are kept constant. The t and
Sig ( p) values give a rough indication of the impact of
each predictor variable namely, Net working capital to
sales ratio (t 3.852, p .005, p< 0.05), Net fixed assets
turnover ratio (t -5.570, p- 0.017, p< 0.05). In connection
with this, the R2
value in terms of these variables is
95.5percent. Overall ANOVA results, the p-value is less
than the 0.01(p<0.01).Hence, this model is statistically
significant and these ratios influences profitability more
than others.

Table 2: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – Heromotor Corporation
S.No. Ratio of
Multiple
Regression
Co-efficient
t’ value p-value
X1 Quick ratio
.965 11.694 .000**
.065 .767 .463
.105 1.030 .330
-.002 -.019 .985
.050 .372 .719
.126 1.263 .238
-.129 -1.458 .179
.027 .285 .782
-.078 -.743 .476
.013 .132 .898
.025 .278 .787
-.001 -.017 .987
ANOVA
Sum of
Squares
df Mean Square F p-val S/NS
Regression .363 1 .363 136.741 .000 S
Residual .027 10 .003
Total .389 11
**significant at 5% level. * Significant at 1% level S- significant NS – Not significant
Model Summary
MODEL R R SQUARE
1 0.965 0.932

It is clear that, the multiple regression co-
efficient values of Heromotor Corporation These
presented values indicate that one variable is
individually contributes significantly to variations in the
current ratio when influence of other variables are kept
constant. The t and Sig ( p) values give a rough
indication of the impact of predictor variable namely,
Quick ratio (t- 11.694, p 0.00, p< 0.01 ). In connection
with this, the R2
value in terms of these variables is 93.2
percent. Overall ANOVA results, the p-value is less than
the 0.01(p<0.01).Hence, this model is statistically
significant and these ratios influences profitability more
than others.
Table 3: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – Mahindra and Mahindra
S.No. Ratio of
Multiple
Regression Co-
efficient
t’ value p-value
X1 Quick ratio
.015 .325 .756
.043 .488 .643
.667 9.469 .000*
.022 .331 .752
.033 .414 .693
.434 6.279 .000*
-.147 -2.379 .049**
.281 4.037 .005*
.045 .685 .519
.005 .075 .943
.001 .009 .993
-.004 -.054 .959

ANOVA
Sum of
Squares
df Mean Square F P-val S/NS
Regression .368 4 .092 165.704 .000** S
Residual .004 7 .001
Total .371 11
Model Summary
MODEL R R SQUARE
1 0.995 0.990
It is clear that, the multiple regression co-
efficient values of MAHINDRA AND MAHINDRA
These presented values indicate that three variables are
individually contributing significantly to variations in
the current ratio when influence of other variables are
kept constant. The t and Sig ( p) values give a rough
indication of the impact of each predictor variable
namely, Net working capital to sales (t 9.469, p .000 ,
p< 0.01), Creditors turnover ratio(t 6.279, p .000 , p<
0.01), Distribution expenses ratio(t -2.379, p .049, p<
0.05 ), Miscellaneous expenditure ratio(t 4.037, p .005,
p< 0.01). In connection with this, the R2
value in terms
of these variables is 99 percent. Overall ANOVA results,
the p-value is less than the 0.01(p<0.01) except
Miscellaneous expenditure .Hence, this model is
statistically significant and these ratios influences
profitability more than others.
V. SUGGESTIONS
To increase company efficiency, management
should strive to maintain optimal working capital, and
excess capital should be invested in various securities on
a timely basis or used to repay advances and short-term
loans. Management should also strive to maintain a
balanced credit and collection policy to reduce bad
debts.
To strengthen a company's liquidity situation,
management should aim to create a cashless system or
have a regular cash level to satisfy short-term
commitments.
To increase a company's solvency, the
management should aim to maintain a balance between
debt and equity in order to prevent financial risk or
overlook the risk of arranging funding on a regular basis.
VI. CONCLUSION
During the study period, the researchers wanted
to learn about the key ratios that influenced the
profitability of Indian automobile companies. According
to the findings of this research, each organisation has
various financial parameters that contribute to
profitability. The finding suggests that in order to
enhance profitability, the firm should focus on its cost of
production, fixed asset investment, and sales turnover. In
order to enhance profit in the long term, management
should aim to keep a tight grip on spending and
disbursement costs.
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