Anis Ali* | Basel J.A. Ali
© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
The Indian textile industry is the most prominent sector of the economy and plays a crucial role in its growth and development. The study is based on secondary data collected from the websites of the leading textile companies in India. The purpose of this study is to determine the disparity between the total resource growth of the top Indian textile companies and its effect on profitability. Financial ratios and statistical tools are utilized to determine the profitability, disparity in resource growth, and its effect on the profitability of the leading textile companies in India. The relationship between total resources and gross profitability (profit before depreciation, interest, and taxes) is concluded to be positive but U-shaped in leading textile companies in India. The governance of profitability on capital employed (ROCE - return on capital employed) is superior to that of profitability on total resources (ROA-return on assets). Based on analysis and findings it is advised to invest in the current assets of the leading Indian textile firms to maximize returns until the profitability of the sales begins to decline (PBDIT) because the relationship between enhancement of resources and the profitability of the sales or gross profitability (PBDIT) is U-shaped.
total resources, profitability, Indian textile, return on assets, return on capital employed, gross profitability
The financial statements of the business organizations indicate the organization's financial performance [1, 2]. The textile industry in India is one of the most significant contributors to the Indian economy. The Indian textile sector has unavoidably expanded during the past few years. The Indian textile sector's exports are increasing each year, allowing the business to reach new heights. The accessibility of production inputs is the Indian textile industry's most significant strength. In addition, the Indian government promotes the Indian textile sector through initiatives such as 'Make in India' and fosters an environment conducive to growth and development. The Indian textile sector is the country's primary source of employment. Due to export, the Indian Textile Industry grows by an average of 18 percent per year. The technological advances and research and development process are crucial to the expansion [3, 4] and prosperity of the Indian textile industry [5]. In spite of this, the Indian textile sector leverages its internal capital to manage its long-term investments. Surprisingly, there is a negative correlation between overall resources and textile enterprises' profitability [6]. Human capital efficiency or managerial effectiveness influences the profitability of Indian textile firms. Intellectual Capital (IC) positively influences the performance of Indian textile companies.
Utilization of employed capital has a significant impact on all financial performance indicators, whereas human capital efficiency has only a marginal impact on the profitability of Indian textile companies. But, structural capital efficiency (SCE) does not significantly influence productivity, profitability, and return on equity [7]. It is evident that there are disparity among the resources of leading Indian textile companies. To know the impact of disparity among the leading Indian textile companies on their profitability, it is necessary to investigate the increasing mismatch between the overall resources and profitability of Indian textile enterprises. Also, investigate the variability of total resources and various measures of profitability, as well as the effect of the growing variability of total resources on the profitability of Indian textile enterprises.
The study will consider the resources and profitability of the of the leading Indian textile companies to add the existing knowledge. Also, the study will establish the relationship between the disparity of resources and its impact on the profitability of business organizations.
The Indian textile industry can expand in Bangladesh and Sri Lanka. By diversifying its product line, India can increase its textile exports to the Korean market. Within the framework of APTA (Asia-Pacific Trade Agreement), the textile markets of China and India have enormous potential. Focusing on investment in SMEs, SEZ, etc., and enhancing innovative technologies in the larger textile industries can help the Indian textile industry face competitive challenges [8]. The Indian textile industry is one of the best industries in the world in terms of value chain productivity. In the industrial textile industry, all raw materials, such as cotton and silk, and skilled labor are readily available. The 'Make in India' initiative of the Indian government accelerates textile production and raises textile owners' income [9]. Indian Textile Industry contributes to job creation and plays a crucial role in social responsibility [10]. Indian textile industry is the second-largest employer and contributes 2 percent to the economy. The innovative machinery and processing, as well as the efficient availability and management of working capital, raise the level of the Indian textile industry. Manpower is another crucial element that has played a crucial role in the development of the textile industry in India [11].
The performance of public textile companies was superior to that of private textile companies. Benefits accrue to public companies as a result of production at a larger scale. The limited liability companies reduce their inputs in the production process by increasing their output [12]. The textile industry contributes significantly to the growth of the Indian economy by increasing employment and exports. They discovered a positive correlation between foreign direct investment (FDI), GDP, job creation, and technological progress [13]. Indian textile companies large, medium, and small studied and discovered that the majority of Indian textile companies face financial difficulties [14]. Indian textile industry planned to manage its long-term investments with funds from internal sources. In addition, they suggested that optimal capital structure and efficient utilization and allocation of funds are advantageous in boosting the financial performance of a textile business organization [15]. A correlation between capital management and financial performance is found in Indian textile firms [16]. The relationship between working capital management and the profitability of textile companies was investigated and discovered that a prudent and aggressive working capital investment policy has a positive impact on the profitability of Indian textile firms [17].
The productivity and financial performance of exporting Indian textile companies are superior to those of non-exporting Indian textile companies. Changes in technology, productivity, and production level have a negative impact on the financial performance and productivity of exporting and non-exporting Indian textile companies. In the Indian textile industry, resources are underutilized and wasted. Utilizing advanced technology in the production process and maximizing resource utilization through large-scale production can increase the productivity and performance of Indian textile firms. The productivity of Indian textile companies could be increased by maximizing the use of limited resources. The training and development of human resources and labor welfare programs play a significant role in the increased productivity and performance of the Indian textile industry [18].
The effect of the firms' resources on the performance of US firms from 2000 to 2019 was studied and discovered that firm resources impact firm performance. The newly established, small-scale, opaque, and liquid firms were minimally governed by the industry resources. While old firms, large-scale firms, firms with low opacity, and firms with low liquidity are heavily governed by industry resources [19]. The ratio of short-term debt to total assets is negatively correlated with profitability in terms of total resources (ROA) and owners' equity (ROE). However, the expansion of total assets has a positive effect on profitability in terms of total resources (ROA) and owners' funds (ROE) [20]. Size and age of the firm, tangible assets, sales growth, operational velocity, and ownership structure influence the financial performance of Indian manufacturing firms [21]. It is evident from the aforementioned studies that no studies exist to explain the disparity in financial growth and its effect on the profitability of the Indian textile industry.
The analysis is based on secondary data gathered from the websites of India's biggest textile firms. The financial information is provided by the financial statements of the pertinent prominent Indian textile enterprises. To determine the resources and profitability of the largest Indian textile firms, financial information such as total assets, PBDIT (profit before depreciation, interest, and tax), ROCE (return on capital employed), and ROA (return on assets) will be selected. PBDIT, (ROCE), and ROA reflect the relational profitability in the context of net sales, capital employed, and total assets [22]. Where,
PBDIT $=\frac{\text { PBDIT }^* 100}{\text { Net Sales }} ;$ ROA $=\frac{\text { Net Income }}{\text { Total Assets }}$
ROCE $=\frac{\text { Net operating profit } * 100}{(\text { Total Assets-Current Liabilities })}$ (1)
where, Gross Revenue=Net Sales-cost of production, PBDIT=Gross Revenue- all expenses excluding interest and depreciation, Net operating profit=Gross Revenue- All operating expenses- depreciation- amortization, Net Income=Net Operating Profit- Interest & Taxes.
To measure the disparity in financial variables among the Indian textile companies’ Analysis of Variance (ANOVA) is applied.
$\mathrm{F}=\frac{\mathrm{Bss} / \mathrm{df1}}{\text { Wss/df2 }} ;$ While, $\mathrm{F} \geq \mathrm{F} \propto$, Reject $\mathrm{H} 0$ (2)
where, F is Fisher's ratio, Bss/df1, and Wss/df2 are the sum of squares between samples divided by degrees of freedom, and the sum of squares within samples divided by degrees of freedom, respectively. The historical growth of the total resources and all measures can be measured by the index numbers. The FBI (fixed base index numbers) explains the growing trend of the variables while CBI (chain based index numbers) in short-term fluctuations of the variables.
$\mathrm{CBI}=\frac{\mathrm{Vcy}}{\mathrm{Vpy}} 100 ; \mathrm{FBI}=\frac{\mathrm{Vcy}}{\mathrm{Vby}} 100$ (3)
where, Vcy=variables of current year, Vpy=variables of previous year, and Vby=variables of base year.
The karl pearson’s correlation matrix is calculated to get the relationship between the growth trend and short term fluctuations of the concerned financial variables.
Karl Pearson's correlation coefficient $\left(\mathrm{r}_{\mathrm{x}, \mathrm{y}}\right)=\frac{c o(x, y)}{\sigma x^* \sigma \mathrm{y}}$ (4)
where, x and y is the trend or fluctuation index numbers of the financial variables of the leading Indian textile companies.
So, the disparity analysis of the growth and development and its impact on the profitability can be bifurcated into two categories.
(1) Disparity analysis
a. Disparity among the total resources
b. Disparity among the PBDIT
c. Disparity among the ROCE
d. Disparity among the ROA
(2) Co- movement analysis
a. Co-movement of total resources and PBDIT
b. Co-movement of total resources and ROCE
c. Co-movement of total resources and ROA
3.1 Hypothesis
Following are the hypothesis of the study:
H01.1: There is no significant difference among the resources of leading Indian textile companies.
H01.2: There is no significant difference among the PBDIT of leading Indian textile companies.
H01.3: There is no significant difference among the ROCE of leading Indian textile companies
H01.4: There is no significant difference among the ROA of leading Indian textile companies
The analysis and interpretations of the disparity in growth and development of the Indian textile companies are bifurcated into two categories:
4.1 Disparity analysis
Disparity analysis reveals the dissimilarities of variables among the similar sector companies over the period. Disparity analysis considers only the significant differences and reveals that the significant differences are due to some financial variations. The disparity of resources and profitability considers the disparity in total assets and disparity in profitability measures i.e. PBDIT, ROCE, and ROA.
From Table 1 analysis of variance (ANOVA), it can be concluded that there is a significant difference in the resources and profitability measures of the leading Indian textile companies. The average investment in the total resources of Arvind, Vardhman, and Welspun is 6361.26, 7196.27, and 6215.82 (Rs. in Crore) while 1909.29, 1041.09, 886.36, 789.92 (Rs. in Crore) in KPR, Page, Nitin, and Rupa textile companies for the period 2011 to 2020 (Appendix 1). There is a significant difference among the PBDIT, ROCE, and ROA of Indian textile companies. The PBDIT of Vardhman, Welspun, KPR, page, and Himastingka is 20.66%, 21.24%, 19.60%, 21.14%, and 22.74% while 14.91%, 12.52%, and 15.49%, and 14.29% of Arvind, Raymond, Nitin, and Rupa companies’ PBDIT, respectively (Appendix 2). The ROCE of the Indian textile companies is significantly different. The ROCE of KPR, Page, and Rupa is 18.27%, 48.83%, and 22.96% while 5.68%, 8.60%, and 9.42% of Raymond, trident, and Nitin companies’ ROCE (Appendix 3). The ROA of Page, Rupa, and KPR is 23.89%, 10.33%, and 10.13%, while 1.32%, 4.09%, and 4.11% of Raymond, Trident, and Arvind companies ROA (Appendix 4). So, the investment in the resources of the Indian textile companies is significantly different. The profitability measures i.e. PBDIT (profit on sales), ROCE (profit on capital employed in the business activities), and ROA (profit on assets) are significantly different in leading Indian textile companies.
4.2 Co-movement analysis
Co-movement analysis of the variables reveals the co-variability of the variables and the impact of independent variables on the dependent variables in form of degree and direction. The degree of the co-movement of the variables reveals the strength of the impact of the independent variables on the dependent variables while direction explains the nature of the relationship i.e. positive or negative. The trend explains growth movement in the long term while the fluctuations in the variables indicate the short-term variations. Normally, it is assumed that the profitability ratio of the business organization remains constant if there is no change in the efficiency, sales price, and cost pattern of the products.
4.2.1 Co-movement of total resources and PBDIT
Co-movement of total resources and PBDIT explain the impact of the movement of the total resources on the gross profitability of the business organization. Positive movement between the total resources and PBDIT indicates the positive impact of increment or decrement of the total resources on the gross profitability (PBDIT).
Table 1. Disparity among the resources of the leading Indian textiles companies (2011-2020)
|
H01 |
Hypothesis |
F* |
Fα** |
Decision: H0 (If F≥F, Reject H0) |
|
H01. 1 |
There is no significant difference among the resources of leading Indian textile companies |
80.32182 |
1.985595 |
Reject. |
|
H01.2. |
There is no significant difference among the PBDIT of leading Indian textile companies |
9.710212 |
1.985595 |
Reject. |
|
H01.3. |
There is no significant difference among the ROCE of leading Indian textile companies |
32.04336 |
1.985595 |
Reject. |
|
H01.4. |
There is no significant difference in the ROA of leading Indian textile companies |
46.05711 |
1.985595 |
Reject. |
Source: * F values (ANOVA) calculated based on the absolute amount of resources and FBI of leading Indian textile companies (as given in appendix 1,2,3 and 4)
Notes: (a): Fα ** taken from the t- table at 5% significance level; (b): F* values are the Fisher's ratios values and calculated using SPSS's calculation.
From Table 2, it is obvious that there is a positive but very low correlation (r=0.004) between the total resources and the PBDIT of the leading Indian textile companies. But, the relational growth movement impact of the total resources on the PBDIT is negative (r=-0.539). This reflects positivity between the total assets and the PBDIT but negativity in the relational growth movement of the total assets and PBDIT. The enhancement or decrement of total assets governs PBDIT negatively in the long run. In the short run, there is a low but positive correlation (r=0.182) between the total assets and PBDIT of the leading Indian textile companies. Also, the relational growth movement impact is positive (r=0.419) in the short run. So, the growth of the total resources has a positive but negligible correlation with the profitability in long run. But, the relational growth movement of total resources is not proportionate to the profitability (PBDIT) in long run. In the short run, the growth of the total resources/ assets affects the profitability positively and their proportional growth is also positive.
4.2.2 Co-movement of total resources and ROCE
Co-movement of total resources and ROCE explain the impact of the movement of the total resources on the profitability of capital employed in operational activities of the business organization. Positive movement between the total resources and ROCE indicates the positive impact of increment or decrement of total resources on the profitability of capital employed in business activities (ROCE).
From Table 3, it is obvious that there is a positive and significant correlation (r=0.857) between the total resources and the ROCE of the leading Indian textile companies. Also, the relational growth movement impact of the total resources on the ROCE is positive (r=0.80). This reflects positivity between the total assets and the ROCE and the relational growth movement of the total assets and ROCE in long run. The enhancement or decrement of total assets governs ROCE positively in the long run. In the short run, there is a low but positive correlation (r=0.233) between the total assets and ROCE of the leading Indian textile companies. Also, the relational growth movement impact is positive (r=0.376) in the short run. So, the growth of the total resources has a positive and significant correlation with the profitability in long run. The relational growth movement of total resources is positive and positively affects profitability (ROCE) in long run. In the short run, the growth of the total resources/ assets affects the profitability positively and their relational growth is low but positive.
From Table 4, it is obvious that there is a positive and low degree correlation (r=0.857) between the total resources and the ROA of the leading Indian textile companies. Also, the relational growth movement impact of the total resources on the ROA is negligible but positive (r=0.80). This reflects positivity between the total assets and the ROA and relational growth movement of the total assets and ROA in long run. The enhancement or decrement of total assets governs ROA positively in the long run. In the short run, there is a low but positive correlation (r=0.238) between the total assets and ROA of the leading Indian textile companies. Also, the relational growth movement impact is positive (r=0.623) in the short run. So, the growth of the total resources has a positive and significant correlation with the profitability (ROA) in long run. The relational growth movement of total resources is positive and positively affects the profitability (ROA) in long run. In the short run, the growth of the total resources/ assets affects the profitability positively and their proportional growth is significant and positive.
Table 2. Co-movement of total resources and PBDIT of leading Indian textile companies
|
Years |
Total TA (Rs. in crores) |
Av. PBDIT |
Trend relationship of TA & PBDIT |
Short term relationship of TA & PBDIT |
||||||
|
FBI (TA) |
FBI (PBDIT) |
+/- of FBI(TA)from 2011 |
+/- of FBI(PBDIT) from 2011 |
CBI (TA) |
CBI (PBDIT) |
+/- of CBI(TA) from P/Y |
+/- of V (PBDIT) from P/Y |
|||
|
2011 |
26163.37 |
15.60 |
100 |
100 |
100 |
100 |
||||
|
2012 |
27948.72 |
18.21 |
107 |
117 |
7 |
17 |
107 |
117 |
7 |
17 |
|
2013 |
30023.07 |
19.07 |
115 |
122 |
15 |
22 |
107 |
105 |
0 |
-12 |
|
2014 |
32139.97 |
18.41 |
123 |
118 |
23 |
18 |
107 |
97 |
0 |
-8 |
|
2015 |
34951.65 |
20.10 |
134 |
129 |
34 |
27 |
109 |
109 |
2 |
12 |
|
2016 |
36487.43 |
19.66 |
139 |
126 |
39 |
26 |
104 |
98 |
-5 |
-11 |
|
2017 |
39939.13 |
17.33 |
153 |
111 |
53 |
11 |
109 |
88 |
5 |
-10 |
|
2018 |
42792.57 |
18.36 |
164 |
118 |
64 |
18 |
107 |
106 |
-2 |
18 |
|
2019 |
43354.02 |
17.05 |
166 |
109 |
66 |
9 |
101 |
93 |
-6 |
-13 |
|
2020 |
43853.21 |
17.32 |
168 |
111 |
68 |
11 |
101 |
102 |
0 |
9 |
|
r=0.004 |
r=-0.539 |
r=0.182 |
r=0.419 |
|||||||
Source: Calculation of FBI and CBI is based on values given in appendix 1 and 2.
Table 3. Co-movement of total resources and ROCE of leading Indian textile companies
|
Years |
Total TA (Rs. in crores) |
Av. ROCE |
Trend relationship of TA & ROCE |
Short term relationship of TA & ROCE |
||||||
|
FBI (TA) |
FBI (ROCE) |
+/- of FBI (TA) from 2011 |
+/- of FBI (ROCE) from 2011 |
CBI (TA) |
CBI (ROCE) |
+/- of CBI (TA) from P/Y |
+/- of V (ROCE) from P/Y |
|||
|
2011 |
26163.37 |
8.82 |
100 |
100 |
100 |
100 |
||||
|
2012 |
27948.72 |
10.35 |
107 |
117 |
7 |
17 |
107 |
117 |
7 |
17 |
|
2013 |
30023.07 |
12.87 |
115 |
146 |
15 |
46 |
107 |
124 |
0 |
7 |
|
2014 |
32139.97 |
13.15 |
123 |
149 |
23 |
49 |
107 |
102 |
0 |
-2 |
|
2015 |
34951.65 |
13.85 |
134 |
157 |
34 |
57 |
109 |
105 |
2 |
3 |
|
2016 |
36487.43 |
20.98 |
139 |
238 |
39 |
138 |
104 |
151 |
-5 |
46 |
|
2017 |
39939.13 |
19.56 |
153 |
222 |
53 |
122 |
109 |
93 |
5 |
58 |
|
2018 |
42792.57 |
23.13 |
164 |
262 |
64 |
162 |
107 |
118 |
-2 |
25 |
|
2019 |
43354.02 |
17.74 |
166 |
201 |
66 |
101 |
101 |
77 |
-6 |
-41 |
|
2020 |
43853.21 |
17.84 |
168 |
202 |
68 |
102 |
101 |
101 |
0 |
24 |
|
r=0.857 |
r=0.80 |
r=0.233 |
r=0.376 |
|||||||
Source: Calculation of FBI and CBI is based on values given in appendix 1 and 3.
Table 4. Co-movement of total resources and ROA of leading Indian textile companies
|
Years |
Total TA (Rs. in crores) |
Av. ROA |
Trend relationship of TA & ROA |
Short term relationship of TA & ROA |
||||||
|
FBI (TA) |
FBI (ROA) |
+/- of FBI (TA) from 2011 |
+/- of FBI (ROA) from 2011 |
CBI (TA) |
CBI (ROA) |
+/- of CBI (TA) from P/Y |
+/- of V (ROA) from P/Y |
|||
|
2011 |
26163.37 |
5.23 |
100 |
100 |
100 |
100 |
||||
|
2012 |
27948.72 |
6.35 |
107 |
122 |
7 |
22 |
107 |
122 |
7 |
22 |
|
2013 |
30023.07 |
8.01 |
115 |
153 |
15 |
53 |
107 |
126 |
0 |
4 |
|
2014 |
32139.97 |
8.35 |
123 |
160 |
23 |
60 |
107 |
104 |
0 |
-22 |
|
2015 |
34951.65 |
9.52 |
134 |
182 |
34 |
82 |
109 |
114 |
2 |
10 |
|
2016 |
36487.43 |
8.66 |
139 |
166 |
39 |
66 |
104 |
91 |
-5 |
-13 |
|
2017 |
39939.13 |
8.06 |
153 |
154 |
53 |
54 |
109 |
93 |
5 |
2 |
|
2018 |
42792.57 |
8.14 |
164 |
156 |
64 |
56 |
107 |
101 |
-2 |
8 |
|
2019 |
43354.02 |
7.30 |
166 |
140 |
66 |
40 |
101 |
90 |
-6 |
-11 |
|
2020 |
43853.21 |
7.55 |
168 |
144 |
68 |
44 |
101 |
103 |
0 |
13 |
|
r =0.414 |
r =0.068 |
r =0.388 |
r =0.623 |
|||||||
Source: Calculation of FBI and CBI is based on values given in appendix 1 and 4.
From the analysis, it is clear that the investment in the resources of the Indian textile companies is significantly different. The profitability measures i.e. PBDIT (profit on sales), ROCE (profit on capital employed in the business activities), and ROA (profit on assets) are significantly different in leading Indian textile companies. The small firms’ resources strongly correlated with the firm performance. Total resources and profitability on sales (PBDIT) are positively correlated [23]. The growth of the total assets positively affects the profitability in the context of total resources (ROA) and owners' funds (ROE) [20]. But, in the long term, proportionate co-movement is of total resources, and PBDIT is negatively correlated. This refers that the enhancement of the total resources in the Indian textile industry affects positively but not in a similar proportion to the enhancement of the total resources. This implies the U-shaped relationship between the total resources and the profitability of sales (PBDIT). In the short run, the total resources and PBDIT is positively correlated. Also, the proportionate change in the total assets moderately and positively affects the profitability of sales (PBDIT) in leading Indian textile companies. The higher resources possession firms enjoy enhanced financial performance [24].
Absolute growth of the total resources has a positive and significant correlation with the profitability of capital employed (ROCE) in long run. The relational growth movement of total resources is positive and positively affects profitability (ROCE) in long run. Also, in the short run, the growth of the total resources/ assets affects the profitability positively and their relational growth is low but positive. Growth of the total resources has a positive and significant correlation with profitability (ROA) in long run. The relational growth movement of total resources is positive and positively affects the profitability (ROA) in long run. In the short run, the growth of the total resources/ assets affects the profitability positively and their relational growth is significant and positive. So, it can be said that the total resources and the profitability measures i.e. PBDIT, ROCE, and ROA are significantly different. The trend of co-variation analysis reveals that enhancement in the total resources governs relational growth of PBDIT positively while the trend reflects the negativity in long run. The short-term co-movement and relational growth of total resources and PBDIT are positively correlated. The correlation between total resources and profitability on capital employed (ROCE); and total resources and profitability (ROA) is positively correlated. But, the movement of total resources and ROCE is strongly correlated to the ROA and PBDIT. Hence, the enhancement of the total resources directly and proportionately governs the ROCE. The positive movement of the total assets/ resources governs the profitability of sales up to a certain extent after that starts to decline. The total assets and the return on total assets are positively but insignificantly correlated.
From all analyses, interpretations, and discussions it is obvious that there are significant differences in the total resources and profitability measures of the leading Indian textile companies. The total resources of the Indian leading companies govern strongly and positively the return on capital employed (ROCE). This refers that the investment in the capital employed enhancing the gross revenue more than the enhancement in the cost of production, all operating expenses, depreciation, and amortization in the leading Indian textile companies. Also, based on the analysis it is observed that the impact of the enhancement of the total resources on the profitability of the sales or gross profitability (PBDIT) is U-shaped and it gives a negative return after a certain level of gross revenue. The co-movement of the total resources and the return on assets (ROA) is moderately and positively correlated. So, the investment in the capital employed or current assets is more profitable than the investment in the long-term resources or fixed assets.
Therefore, it is advisable to invest in the current assets of the leading Indian textile companies to get maximum returns until it starts the decline in the profitability of sales (PBDIT) due to the U-shaped relationship between the total resources and the profitability of sales (PBDIT). Because current assets are also ingredients of the total resources. The study considers only quantitative factors i.e. total resources and measures of profitability. Old firms, big scale firms, low opacity, and low liquidity firms are strongly governed by the industry resources [19]. There is a need to consider some other quantitative and qualitative factors that may affect profitability. Further, there is scope to study the governance of profitability in leading Indian textile companies by the resources based on the age and production level of firms, liquidity, etc.
Appendix 1: Total resources/ total assets of leading Indian textile companies (Rs. in crores)
|
Year |
Arvind |
Vardhman |
Welspun |
Raymond |
Trident |
KPR |
Page |
Nitin |
Rupa |
Himastingka |
|
2011 |
4620.78 |
5180.01 |
6247.76 |
2950.87 |
3266.6 |
1557.16 |
371.53 |
347.3 |
487.46 |
1133.9 |
|
2012 |
5283.72 |
5923.32 |
6248.49 |
3021.11 |
3286.89 |
1587.77 |
471.73 |
345.73 |
602.62 |
1177.34 |
|
2013 |
6033.51 |
6880.19 |
6263.88 |
3041.18 |
3184.53 |
1685.69 |
669.6 |
353.98 |
637.41 |
1273.1 |
|
2014 |
6652.42 |
6304.82 |
6221.22 |
3183.29 |
4503.89 |
1731.4 |
823.46 |
609.9 |
738.97 |
1370.6 |
|
2015 |
7083.04 |
6985.33 |
6097.77 |
3464.11 |
5623.77 |
1730.43 |
944.7 |
635.04 |
677.31 |
1710.15 |
|
2016 |
6457.93 |
6958.53 |
6247.76 |
3606.2 |
6150.36 |
1800.25 |
1154.1 |
885.22 |
711.46 |
2515.63 |
|
2017 |
7044.07 |
7695.96 |
6248.49 |
3993.88 |
6129.07 |
1930.37 |
1412.4 |
928.59 |
894.01 |
3662.31 |
|
2018 |
7013.88 |
8369.12 |
6263.88 |
4257.21 |
6103.24 |
2397.21 |
1350.6 |
1436.28 |
999.68 |
4601.48 |
|
2019 |
6814.55 |
8726.68 |
6221.22 |
4678.41 |
5718.35 |
2187.2 |
1512.9 |
1645.26 |
992.09 |
4857.32 |
|
2020 |
6608.73 |
8938.75 |
6097.77 |
4423 |
5754.16 |
2485.43 |
1699.9 |
1676.32 |
1158.18 |
5011.01 |
|
Mean |
6361.26 |
7196.27 |
6215.82 |
3661.93 |
4972.09 |
1909.29 |
1041.1 |
886.36 |
789.92 |
2731.28 |
Source: Based on financial statements available on the website of the concerned Indian textile companies.
Appendix 2: PBDIT (Profit before depreciation, Interest and tax) of leading Indian textile companies
|
Year |
Arvind |
Vardhman |
Welspun |
Raymond |
Trident |
KPR |
Page |
Nitin |
Rupa |
Himastingka |
|
2020 |
11.68 |
16.04 |
19.56 |
7.91 |
18.29 |
23.77 |
19.27 |
15.95 |
20.91 |
19.86 |
|
2019 |
11.53 |
16.67 |
19.76 |
12.02 |
18.41 |
17.63 |
18.91 |
11.95 |
14.07 |
29.52 |
|
2018 |
11.45 |
21.04 |
16.27 |
12.26 |
19.71 |
18.27 |
22.9 |
14.55 |
15.81 |
31.31 |
|
2017 |
10.91 |
17.83 |
19.07 |
11.11 |
20.01 |
17.71 |
22.03 |
13.72 |
15.04 |
25.91 |
|
2016 |
13.73 |
30.91 |
25.55 |
10.19 |
21.48 |
21.3 |
20.56 |
14.38 |
13.32 |
25.14 |
|
2015 |
16.91 |
24.03 |
28.36 |
12.59 |
20.38 |
19.89 |
21.49 |
17.96 |
11.83 |
27.59 |
|
2014 |
18.28 |
19.15 |
25.69 |
13.35 |
18.51 |
18.18 |
21.23 |
16.91 |
12.77 |
20.01 |
|
2013 |
18.4 |
25.62 |
22.97 |
15.21 |
19.22 |
19.48 |
21.7 |
19.3 |
13.44 |
15.4 |
|
2012 |
17.99 |
21.22 |
16.37 |
13.05 |
17.32 |
24.32 |
21.12 |
19.62 |
14.12 |
16.93 |
|
2011 |
18.23 |
14.12 |
18.82 |
17.54 |
11.72 |
15.49 |
22.15 |
10.54 |
11.63 |
15.75 |
|
Mean |
14.91 |
20.66 |
21.24 |
12.52 |
18.51 |
19.60 |
21.14 |
15.49 |
14.29 |
22.74 |
Source: Calculation based on the data extracted from the financial statements available on the website of the concerned Indian textile companies.
Appendix 3: ROCE (Return on Capital Employed) of leading Indian textile companies
|
Year |
Arvind |
Vardhman |
Welspun |
Raymond |
Trident |
KPR |
Page |
Nitin |
Rupa |
Himastingka |
|
2020 |
6.96 |
7.6 |
20.15 |
-0.21 |
12.52 |
30.84 |
48.92 |
14 |
30.67 |
6.99 |
|
2019 |
13.2 |
10.22 |
16.59 |
10.14 |
13.21 |
25.45 |
51.85 |
7.47 |
17.81 |
11.48 |
|
2018 |
13.41 |
16.68 |
11.5 |
18.65 |
15.49 |
27.19 |
76.19 |
11.04 |
27.1 |
14.05 |
|
2017 |
12 |
13.37 |
6.87 |
13.35 |
11.47 |
22.73 |
59.08 |
13.4 |
29.52 |
13.85 |
|
2016 |
16.73 |
28.74 |
6.78 |
1.97 |
12.29 |
27.64 |
56.68 |
13.71 |
28.3 |
16.98 |
|
2015 |
7.11 |
13.67 |
17.02 |
3.73 |
5.62 |
13.49 |
36.61 |
8.91 |
18.93 |
13.42 |
|
2014 |
9.14 |
7.77 |
17.38 |
4.87 |
3.9 |
12.65 |
38.33 |
8.2 |
18.21 |
11.06 |
|
2013 |
9.34 |
12.89 |
1.15 |
4.12 |
10.88 |
11.35 |
40.33 |
12.17 |
19.33 |
7.09 |
|
2012 |
7.98 |
7.18 |
7.69 |
-2.62 |
2.79 |
8.67 |
39.68 |
5.21 |
21.92 |
4.97 |
|
2011 |
15.58 |
2.65 |
5.55 |
2.77 |
-2.18 |
2.67 |
40.67 |
0.11 |
17.76 |
2.64 |
|
Mean |
11.15 |
12.08 |
11.07 |
5.68 |
8.60 |
18.27 |
48.83 |
9.42 |
22.96 |
10.25 |
Source: Calculation based on the data extracted from the financial statements available on the website of the concerned Indian textile companies.
Appendix 4: ROA (Return on Assets) of leading Indian textile companies
|
Year |
Arvind |
Vardhman |
Welspun |
Raymond |
Trident |
KPR |
Page |
Nitin |
Rupa |
Himastingka |
|
2020 |
1.4 |
3.92 |
8.63 |
-2.67 |
6 |
17.4 |
20.03 |
4.1 |
15.61 |
1.07 |
|
2019 |
2.51 |
6.25 |
7.63 |
2.01 |
5.97 |
13.46 |
22.68 |
1.44 |
8.07 |
3.01 |
|
2018 |
2.84 |
8.31 |
2.26 |
1.73 |
6.07 |
12.06 |
29.16 |
4.46 |
9.34 |
5.12 |
|
2017 |
3.54 |
7.09 |
4.86 |
2.45 |
4.33 |
11.65 |
24.56 |
5.64 |
10.58 |
5.87 |
|
2016 |
0.28 |
14.39 |
4.9 |
0.93 |
5.47 |
13.24 |
23.07 |
6.47 |
10.84 |
7.02 |
|
2015 |
4.5 |
9.68 |
12.09 |
2.36 |
4.06 |
9.02 |
24.62 |
6.95 |
11.5 |
10.4 |
|
2014 |
5.67 |
5.69 |
10.56 |
3.14 |
2.61 |
8.43 |
23.8 |
6.71 |
8.89 |
7.97 |
|
2013 |
5.98 |
9.47 |
0.72 |
2.89 |
6.18 |
7.74 |
22.96 |
9.82 |
9.73 |
4.62 |
|
2012 |
4.94 |
5.46 |
5.1 |
-1.58 |
1.5 |
6.34 |
23.85 |
4.08 |
10.24 |
3.59 |
|
2011 |
9.39 |
2.11 |
3.64 |
1.9 |
-1.33 |
2 |
24.21 |
0.08 |
8.51 |
1.76 |
|
Mean |
4.11 |
7.24 |
6.04 |
1.32 |
4.09 |
10.13 |
23.89 |
4.98 |
10.33 |
5.04 |
Source: Calculation based on the data extracted from the financial statements available on the website of the concerned Indian textile companies.
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