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Analysis of Financial Performance Parameters of Four Automobile

Companies in India By MCDM Techniques

Debangshu Mukherjee

, Dr.Avneesh Kumar

, Dr.Somarata Chakraborty

Business Administration, IQ City United World School of Business, Kolkata

Dr.K.N. Modi University, Newai, Rajasthan ,

Business Administration, IQ City United World School of Business, Kolkata

DOI: https://dx.doi.org/10.51584/IJRIAS.2025.10100000102

Received: 12 October 2025; Accepted: 20 October 2025; Published: 11 November 2025

ABSTRACT

A research investigation into the financial performance of selected automobile companies in India examines

their overall financial stability by assessing essential indicators such as profitability, liquidity, and solvency.

The objective is to uncover patterns, highlight strengths, and pinpoint areas needing improvement within the

industry, ultimately providing valuable information for investors and other stakeholders. The study aims to

analyze both the financial structure and position of these Indian automobile firms. This type of research

concentrates on observing existing conditions as well as exploring potential new insights and interpretations.

For this analysis, data was sourced from secondary materials, including annual reports, books, online

resources, magazines, and newspapers. The study employed tools of MCDM (Multi Criteria Decision making)

focusing mainly on TOPSIS method is employed here. By evaluating the financial performance of the selected

companies, this research enhances our understanding of the sector’s economic landscape and establishes a

foundation for fostering competitiveness and sustainable growth in an ever-evolving global marketplace. The

main objective of this study is to analyze the financial performance of four market leader in automobile sector

since last 5 years. The company taken for analysis are Tata Motors, Maruti Suzuki, Hyundai and Mahindra &

Mahindra.

INTRODUCTION

Automobile industry contributes is a major and significant contributor to Indian economy. It contributes

approximately 7.1% to the GDP and employing over 37 million individuals directly and indirectly (Wikipedia).

As per the data of 2025 India has ascended as the third largest automobile market globally, just behind China

and United States. The industry encompasses a diverse range of vehicles, including two-wheelers, passenger

cars, commercial vehicles, and electric vehicles (EVs) (indbiz.gov.in). Notably, India is the world's largest

manufacturer of two-wheelers and tractors. Electric vehicles are gaining momentum and will become third

largest EMarketer by 2025 (Economic times, Apr 28, 2024). India's automotive market is projected to reach

$300 billion by 2026, driven by rising income levels, urbanization, and an expanding middle class. In March

2024, the Indian auto industry produced 2,325,959 units, including passenger vehicles, three-wheelers, two-

wheelers, and quadricycles. Passenger vehicles accounted for 368,086 units, three-wheelers 56,723 units, and

two-wheelers 1,487,579 domestically. The Indian automotive industry produced 7,394,417 units in Q1 2024,

with passenger vehicles, commercial vehicles, three-wheelers, and two-wheelers leading the pack. The

industry's growth was fueled by foreign direct investment (FDI), with a cumulative equity FDI inflow of

around $35.40 billion between 2000 and 2023. Government initiatives also contributed to growth, with total

automobile exports reaching 47,61,487 units in FY23, contributing to the nation's GDP and employing 19

million people. India's transition to electric vehicles (EVs) is gaining momentum, with projections suggesting

it will become the third-largest EV market by 2025. This presents a substantial investment opportunity of over

$200 billion over the next 8-10 years, with the EV market forecasted to grow at a CAGR of 49% between 2022

and 2030(Economic Times, August 6). Passenger vehicle sales performance In 2024, it was estimated that 82

million passenger vehicles were sold worldwide, a 3% increase over 2023. Approximately 65% of passenger

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car sales were concentrated in important markets including China, the United States, India, Japan, and

Germany. China leads the world in passenger vehicle sales with 34%, followed by the United States (18%),

India (5.2%), Japan (4.6%), and Germany (3%). India is still the world's third-largest market for passenger

cars, after the United States and China.(SIAM-Annual-Report-24-25, n.d.)

Sources: SIAM-Annual-Report-24-25

LITERATURE REVIEW

This literature review examines the financial performance of Indian automobile companies using various

analytical techniques. Multiple studies employ ratio analysis and ANOVA to evaluate key financial indicators

such as liquidity, solvency, and profitability (Kavitha S Sharma, 2025; Dr. Kalpesh K. Chauhan, 2023).

Advanced multi-criteria decision-making (MCDM) techniques, including fuzzy multi-objective optimization

on the basis of ratio analysis (F-MOORA) and fuzzy step-wise weight assessment ratio (F-SWARA), are

utilized to assess both accounting-based and value-based financial performance measures ((Agrawal et al.,

2024; Jain et al., 2018; Promethee, n.d.)). The automotive sector's significance to India's economic growth is

highlighted, with projections indicating it could contribute 12% to GDP by 2026

((A_Study_of_Investment_Pattern_of_Central, n.d.)). Despite recent challenges, the industry shows resilience

and potential for recovery (Samita Mahapatra, 2021). These analyses provide valuable insights for stakeholders

and identify areas for improving competitiveness in the global market ((K. S. Sharma, n.d.; M. P. Sharma &

Grover, 2016). Finding a company's strengths and shortcomings through financial performance analysis is one

way to evaluate its financial health. Balance sheets and profit and loss accounts are the main financial

statements upon which it is based. Financial performance can be analyzed with the help of ratio and trend

analysis tools. Researchers, creditors, shareholders, directors, and investors are among the stakeholders who

utilize it to determine a company's present financial status ((Narayan Konwar, n.d.).

TOPSIS Process is designed as follows:

Step-1 Create an evaluation matrix consisting of m alternatives and n criteria, with the

Step-2 The matrix

󰇛



󰇜

mxn

is then normalised to form the matrix

R = (

)

m x n























, i=1, 2……., m, j=1,2, 3…...,n



= r

ij .

, i= 1,2,….m , j= 1,2,3,…..n

Where w

= W









,j = 1.2…..n so that









= 1 and W

is the original weight given to the indicator

j ,

j = 1,2,…n

Category

2020-21

2021-22

2022-23

2023-24

2024-25

Passenger car

15,41,866

14,67,039

17.47.376

15,48,947

13.53,287

Utility vehicle

10,60,750

14,89,219

20,03,718

25,20,691

27,97,229

Vans

1,08,841

1,13,265

1,39,020

1,49,112

1,51,332

Total

Passenger

Vehicle

27,11,457

30,69,523

38,90,114

42,18,750

43,01,848

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Step -4: Determine the worst alternative denoted by (A

) and the best alternative by (Ab )

w =

{max ( t

| i= 1,2,…m )| j  J

, min ( t

| i= 1,2,…m )| j  J

}󰇝

| j=1,2,…n}

= {min ( t

| i= 1,2,…m )| j  J

, max ( t

| i= 1,2,…m )| j  J

}󰇝

| j=1,2,…n}

Where

= { j= 1, 2,…n | j } associated with the criteria having negative impact

= { j= 1, 2,…n | j } associated with the criteria having positive impact

Step -5: calculate L

– distance between the target alternative i to the worst condition A





󰇛





– t

)

i= 1, 2…m

and the distance between the alternative i and the best condition A





󰇛





– t

)

i= 1, 2,…m

where d

and d

are L

-norm distances from the target alternative i to the worst and best condition

respectively.

Step -6: calculate the similarity to worst condition:

= d

/ (d

+ d

) , 0 ≤ s

≤ 1, i = 1,2,…,m

=1 if and only if the alternative solution is the best condition

=0 if and only if the alternative solution is the worst condition

Step 7: Rank the alternatives according to s

(i=1,2, 3,,m)

Objectives of the Study

1. To compare the Liquidity position of the five selected Automobile Industry.

2. Assessing the financial performance by Multi Criteria Decision making model (MCDM) using

Shannon weight method.

RESEARCH METHODOLOGY

Selection of Sample

To evaluate the financial performances of automobile companies I have selected 4 leading automobile

companies in India like Maruti Suzuki Ltd, TATA Motors, Hyundai Motor, and Mahindra &Mahindra.

Sources of Data: The study is done from secondary data. The data is gathered from money control .com and

company website.

Period of Study: The study is conducted for the period March 2021- March 2025.

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Research Framework Proposal

Table -1

Descriptions

Decision Criterion

Investment Valuation Ratios

 Net Profit Margin (NPM)

 Return on Capital (ROC)

 Return on Net Worth

(RONW)

Liquidity and Solvency

Ratios

 Current Ratio (CR)

 Quick Ratio (QR)

 Financial Change Coverage

Ratios (FCC)

Management Efficiency

Ratios

 Investment Turnover Ratio

(ITR)

 Total Asset Turnover Ratio

(TATR)

Profit & Loss Account Ratio

 Selling Distribution Cost

Composition (SDC)

 Expenses as Composition of

total Sales (ETC)

The research's framework proposal, which comprises goals, criteria for making decisions, and alternatives for

making decisions, is shown in Table 1. As decision criteria, ten sets of well-known financial ratios are set. The

four categories of financial ratios are profitability, solvency, liquidity, and efficiency ((Acosta-González et al.,

n.d.). The ability of the business to fulfil its short-term obligations is indicated by a liquidity ratio, which

makes it significant. It can quickly turn its current assets into cash. The solvency ratio highlights a company's

long-term viability by assessing its capacity to meet its long-term financial obligations. In actuality,

profitability ratios—which show how much value a company creates—are the basis for speculation by

investors and shareholders. Common financial ratios, by R. Messer. (Page 325 of Emerald Publishing Limited's

Financial Modelling for Decision Making: Using MS-Excel in Accounting and Finance, Bingley, UK, 2020.)

All four types of financial ratios are employed in this study: CR, QR, and FCCR are primarily used to examine

liquidity and solvency ratios; NPM, ROC, and RONW are used to examine profitability ratios; ITR and TATR

are used to examine efficiency; and SDC and ETC are used to monitor a company's profit and loss.

Decision Alternatives

Maruti Suzuki, TATA Motors, Hyundai Motor, and Mahindra &Mahindra.

Proposed TOPSIS Model

Upon collection of various financial ratio data of five companies from moneycontrol.com,

Shannon’s entropy method is applied to calculate information’s about weight of decision criteria.

However, as the entropy value increases, the entropy weight decreases, indicating less information and lower

significance of the criteria in a research study, and vice versa. Furthermore, Shannon’s entropy has garnered

significant attention in TOPSIS studies. The entropy method serves as a standard approach for determining

attribute weights based on the variability of data among alternatives ((Chai et al., 2019)). The concept entropy

originated with Rudolp Clausius in 1865 as a response to the observation that a portion of functional energy

produced by combustion processes is inevitably lost through dissipation, failing to be converted into useful

work. In this method m indicators and n samples are set in the evaluation and measured value of ith indicator

in jth value is recorded as x

. The Shannon Entropy Weight Method (EWM) is a technique used to determine

the weight of the criterion in decision making assigning greater weights to the criterion with greater variability

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or importance. Entropy concept can be considered as a criterion for the degree of uncertainty represented by a

discrete probability distribution The first step is standardization of the values. Let the standardized value of ith

index in the jth sample is denoted by p

and calculation method is as follows:

Step-1: p











In the Step -2 Computation of the entropy measure of the project outcome using the equation

=-k





 







in which K= 1/ln(m)

Step-3: The objective weight based on the entropy concept is







󰇛

󰇜





In original situation p

ln p

=0 is set when p

=0 for convenience in calculation.

Table 2:

Key Financial

Ratios

Net

profit

Marg

Retu

rn on

capit

Retu

rn on

Net

Wort

Curre

Ratio

Quic

ratio

Financ

ial

charge

covera

ratio

Investm

ent

Turnove

r Ratio

Total

Assets

Turnov

er ratio

Selling

Distributi

on Cost

composit

ion

Expenses

composit

ion of

Total

Sales

Maruti Suzuki

India

9.18

20.6

15.7

0.81

0.65

116.7

1.91

1.93

0.73

6.52

Honda India

Power

10.06

18.1

11.5

5.07

4.35

319.11

1.73

0.24

37.95

Tata Motors

10.61

20.9

26.2

0.51

0.59

9.63

1.67

1.76

0.45

4.19

Mahindra

&Mahindra

10.85

25.3

20.0

1.14

0.92

122.94

1.89

1.97

0.57

3.92

Sources: Moneycontrol.com

Table 3 below shows the benefit criterion and negative criteria of the financial parameters.

Table -3

MAX

MIN

NPM

ROC

RONW

FCC

ITR

TATR

SDC

ETC

Table -4 depict the standardization of the values. Normalization enables more accurate and better decision

making .The standardized value of ith index in the jth sample is denoted by p

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Table -4 Normalization Matrix (p

)

0.22555

2826

0.24240

9979

0.21393

5765

0.10756

9721

0.09984

639

0.20532

0384

0.26527

7778

0.26116

3735

0.36683

4171

0.12400

1521

0.24717

4447

0.21310

8967

0.15704

9537

0.67330

6773

0.66820

2765

0.56143

7771

0.24027

7778

0.23410

0135

0.12060

3015

0.72175

7322

0.26068

7961

0.24617

5571

0.35669

0.06772

9084

0.09062

0.01694

289

0.23194

4444

0.23815

9675

0.22613

0653

0.07968

8094

0.26658

4767

0.29830

5484

0.27231

8998

0.15139

4422

0.14132

1045

0.21629

8955

0.2625

0.26657

6455

0.28643

2161

0.07455

3062

*Authors calculations

Entropy quantifies the uncertainty or randomness in a system. In the context of the Shannon method for

normalization, entropy helps to determine the relative importance of each criterion.

Table: 5 Computation of Entropy measure

-0.3359

-0.3435

-0.3299

-0.2398

-0.2301

-0.3251

-0.3520

-0.3506

-0.3679

-0.2588

-0.3455

-0.3295

-0.2907

-0.2663

-0.2694

-0.3241

-0.3426

-0.3399

-0.2551

-0.2353

-0.3505

-0.3451

-0.3677

-0.1823

-0.2176

-0.0691

-0.3389

-0.3417

-0.3362

-0.2016

-0.3524

-0.3608

-0.3542

-0.2858

-0.2765

-0.3312

-0.3511

-0.3524

-0.3581

-0.1936

sum

-1.384

-1.379

-1.343

-0.974

-0.994

-1.049

-1.385

-1.317

-0.889

0.6012

0.5988

0.5831

0.4231

0.4315

0.4558

0.6014

0.5721

0.3862

1-Eij

0.3988

0.4012

0.4169

0.5769

0.5685

0.5442

0.3986

0.4279

0.6138

*Author’s Calculations

The degree of importance of each criterion is indicated by its weights, A higher weight indicates greater

importance and more significant impact on decision making. Weights of various criterion are shown below.

Shannon weight calculation technique is used in the study.

Table -6 (Weights Calculated)

wij

0.0840

0.0845

0.0879

0.1216

0.1198

0.1147

0.0840

0.0902

0.1293

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Table-7 Rank Analysis

*Calculated using TOPSIS Model by using R Programming.

*Authors calculations

Result and Implications: It is visible from the above table (Table No. 7) that TATA Motors is more consistent

in maintaining its highest position for the years 2022 and 2021 and 2025 and there is a consistency in

maintenance of the financial performance of Manindra &Mahindra along with Hyundai Motors.

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MAHINDRA

&MAHINDRA

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