Optimizing Financial Analysis in the Indian Logistics Industry:
A Principal Component Analysis Approach
Prof. (Dr.) Shweta Gupta,
Department of Accounting and Taxation,
School of Commerce,
IIS (Deemed to be University) Jaipur
e-mail: dr.shwetagupta42@gmail.com,
Shashi Goel,
Research Scholar,
Department of Accounting and Taxation,
School of Commerce,
IIS (Deemed to be University) Jaipur
Abstract
Logistics is one of the most significant segments that boosts economic growth, grows exports through global supply chains, and creates employment. An analysis of the financial performance of the Indian logistics sector through Principal component analysis is very important. This research discusses the use of limited and most important financial ratios that can be best in analysing the performance with little loss of information for saving efforts and time of the analysers.
A total of 43 ratios grouped in four categories were calculated using the Prowess IQ database for seven companies for a period of 9 years from 2014-15 to 2022-23. Comprehensive score of financial performance through Principal component analysis (PCA) was calculated for ranking the companies using SPSS and MS Excel.
In the end, the study concludes that only 25 ratios instead of too many ratios can be used to analyze financial performance using a comprehensive score. PCA resulted in only 5 components, profitability margin, profitability return, liquidity, cash and cash equivalent, and cash from operation, as significant with 93.42% of the cumulative percentage.
This study will help the managers of logistics companies to frame strategic policies using their comprehensive evaluation of financial performance and improve the performance as well as other stakeholders who are interested in analyzing financial performance.
Keywords:Financial performance, financial ratios, Principal component analysis, comprehensive score, Logistics sector.
Introduction
Financial performance is a comprehensive evaluation of a firm's economic viability, solvency, profitability, and efficiency, as reflected in its financial statements and relevant quantitative indicators. It encompasses the analysis of key financial ratios, such as liquidity, leverage, profitability, and efficiency ratios, along with an examination of financial statements, cash flow patterns, and market valuation metrics. Financial performance analysis serves as a fundamental tool for investors, analysts, and decision-makers, contributing to informed investment strategies, risk management practices, and strategic planning for organizations.
Researchers often employ various quantitative models and statistical techniques to assess and compare financial performance, providing insights into a company's ability to generate returns for its stakeholders, manage financial risks, and sustain long-term growth. Ratio analysis is one of the best techniques for evaluating the performance of companies. When companies evaluate their performance, it is impractical to take all of their financial ratios into consideration. To evaluate the financial performance of a company, only a fraction of the available financial ratios is considered and selected as evaluation criteria.
This research paper evaluates the financial performance of selected logistics companies in India as logistics is the life line of every nation’s industry and economy. To avoid heterogeneous behaviour of ratios, companies belonging to single type of industry are selected for the analysis. In this framework, the companies belonging to the logistics services providers of India is identified for the study. Logistics is one of the most significant segments that boosts economic growth, grows exports through global supply chains and creates employment. Indian Logistics sector is unorganized and highly fragmented, still 14 % of the Gross Domestic Product (GDP) is spent on this sector. This sector provide employment to 2.2 crore people in India and is expected to create another 1.2 million jobs by 2025. Noteworthy improvements have been witnessed in India's standing on the World Bank's Logistics Performance Index (LPI), climbing from the 54th position in 2014 to an impressive 38th in 2023. Forecasts paint a compelling picture, anticipating the market to skyrocket to an impressive US$ 380 billion by 2025, maintaining a robust year-on-year growth rate of 10%-12%. Vision@ 2047 is a guiding principle which is being supported by multiple regulatory and government initiatives to revamp India’s logistics sector. It aims to set specific targets to transition India into a developed nation by 2047.
Review of Literature
Financial performance analysis using ratios has been one of the most commonly used primary models of assessment of a firm’s performance over years as well as comparing it to the rest of the players in the industry. It serves as a fundamental tool for investors, analysts, and decision-makers, contributing to informed investment strategies, risk management practices, and strategic planning for organizations. Purba&Septian, (2019) analyzed the short-term financial performance of an energy service company in Indonesia and concluded that the profitability of the company is still relatively low. Hofmann, E., & Lampe, K. (2013) examined the financial statements of 150 publicly quoted logistics service providers (LSPs) from all over the world and concluded that the most important financial indicators positively influencing the profitability measured through return on assets (ROA). Equity ratio and net profit margin were the most significant key macro-financial factors of high profitability. Arif et al. (2016) analyzedthe performance of the tourism and hospitality sector of Bangladesh using Ratio analysis, trend analysis, and capital budgeting techniques and proved that the tourism and hospitality sector of Bangladesh has an enlightening and profitable future which attracts many new investors to invest. Shah, (2020) evaluated the financial statements of selected pharmaceutical companies in India through ratio analysis and inter-firm comparison and indicated that Cadila was doing much better on its EPS, while Sun Pharma noticed the greater and consistent rise in the financials and ratios. Arini et al., (2021) in their paper analyzed the effects of different ratios on the financial difficulties of textile and garment companies and revealed that liquidity ratios and leverage ratios were not significant but influential to financial distress. Mathiraj et al., (2019) measured the financial performance of leading logistics companies in India using ratio analysis and ANOVA was applied and concluded that financial performance could be evaluated with the help of liquidity, solvency and profitability ratios. Jang & Ahn, (2021) analysed financial ratios and concluded that there are differences in financial factors that affect return on assets and return on equity by business types in the logistics industry.
Owing to time constraints faced by financial statement analysts and the inherent correlation of these ratios, it is necessary to narrow down the number of ratios under evaluation to concentrate attention on a select few with the least amount of data loss (Taylor, 1986). Pinches et al., (1973) stated that factor analysis is one such tool that helps to identify key ratios for a set of observed variables from a bigger basket of ratios. Mbona&Yusheng, (2019) analyzed the financial performance of the Chinese Telecoms Industry by applying principal component analysis and recommended a combination 12 of ratios instead of 17, that best analyzes performance in the industry with limited loss of information. Puri et al., (2022) assessed the financial performance of the Indian Automobile Industry and concluded that financial performance can be assessed using just five ratios rather than an expensive study of a large number of ratios using principal components analysis. Kumar, (2022) developed an effective hedging strategy for the US treasury bond portfolio using principal component analysis to reduce the dimensionality of the dataset with minimum loss of information. Tang & Aldulaimi, (2022) in their paper concluded that ranking and scoring of companies can be done based on principal component analysis. F values were calculated for all 11 companies and arranged in ranks. Fitriyana et al., (2020) used the Principal Component Analysis in the consumer goods industry in Indonesia. PCA reduced the 18 variables into five principal components only that influence the stock price the most. Potential investors can invest in shares after analyzing these five components. Wang, (2021) applied factor analysis and principal component analysis to evaluate the performance of 44 retail enterprises from 2016 to 2018. Liu & Bai, (2021) evaluated the financial performance of electric power listed companies in China using principal component analysis. Eleven indicators declined to four principal components and ranks were assigned based on comprehensive financial performance score. Han & Ren, (2020) and Guohua & Wenxing, (2020) used factor analysis to develop a financial risk assessment model in China based on the principal factor score, indicating that a company with negative factor values faces higher financial risks.
Although there are many case studies on ratio-based financial performance analysis in the reviewed literature, there are still certain gaps.
First, we have found that very few studies have so far focused on the Indian logistics sector. Second, a small number of studies have used PCA to determine which ratios, out of the pool of all ratios in India, provide the greatest performance analysis with the least amount of data loss. Third, rare studies focused on ranking of the companies based on comprehensive scores (Liu & Bai, 2021). By applying PCA we create new independent variables that allow for effective further analysis with even fewer variables.
Research Objectives
Research Methodology:
Sampling Method and data
The research paper analyses the logistics sector for nine years from 2014-15 to 2022-23. Sample companies were chosen based on market capitalization. CMIE Prowess IQ database was used to collect secondary data. Below is the list of companies whose market capitalization was available in March 2023. Information about 29 companies, listed on the Bombay Stock Exchange, were available. Of these 29 companies, 97.86% of the total market capitalization of the logistics sector has been taken represented by the top 8 companies. Because of the non-availability of data, Tiger Logistics (India) Ltd. was excluded from the further analysis. Finally, the collated secondary data comprise of 7 companies, 43 ratios, for 9 years. The analysis was conducted using SPSS.
|
S.No. |
Name of the Company |
Market Capitalization (Rs.) |
Market Capitalization (%) |
Cumulative Market Capitalization % |
|
1 |
Blue Dart Express Ltd. |
147206.9 |
40.66037 |
40.66037297 |
|
2 |
Allcargo Logistics Ltd. |
87332.47 |
24.12231 |
64.7826819 |
|
3 |
Transport Corporation Of India Ltd. |
48698.21 |
13.45105 |
78.23373014 |
|
4 |
Sindhu Trade Links Ltd. (1992) |
26459.5 |
7.308441 |
85.54217147 |
|
5 |
Mahindra Logistics Ltd. |
25469.07 |
7.034872 |
92.57704378 |
|
6 |
Reliance Industrial Infrastructure Ltd. |
11811.22 |
3.262405 |
95.83944891 |
|
7 |
Tiger Logistics (India) Ltd. |
3840.99 |
1.060929 |
96.90037788 |
|
8 |
ShreejiTranslogistics Ltd. |
3483.34 |
0.962142 |
97.86251949 |
|
9 |
Arshiya Ltd. |
1277.86 |
0.352961 |
98.21548023 |
|
10 |
Sanco Trans Ltd. |
1258.11 |
0.347506 |
98.56298579 |
|
11 |
Flomic Global Logistics Ltd. |
622.94 |
0.172064 |
98.73504952 |
|
12 |
East West Holdings Ltd. |
528.05 |
0.145854 |
98.88090347 |
|
13 |
Future Supply Chain Solutions Ltd. |
499.4 |
0.13794 |
99.01884393 |
|
14 |
Sical Logistics Ltd. |
464.07 |
0.128182 |
99.1470258 |
|
15 |
Cargosol Logistics Ltd. |
418.2 |
0.115512 |
99.26253782 |
|
16 |
A B C India Ltd. |
416.59 |
0.115067 |
99.37760513 |
|
17 |
Aqua Logistics Ltd. |
407.99 |
0.112692 |
99.49029702 |
|
18 |
Chartered Logistics Ltd. |
372.52 |
0.102895 |
99.59319165 |
|
19 |
Cargotrans Maritime Ltd. |
293.76 |
0.08114 |
99.6743318 |
|
20 |
V I F Airways Ltd. |
252.1 |
0.069633 |
99.74396494 |
|
21 |
M F L India Ltd. |
237.79 |
0.065681 |
99.80964548 |
|
22 |
Bhoruka Steel & Services Ltd. |
219.61 |
0.060659 |
99.87030448 |
|
23 |
Quality R O Inds. Ltd. |
204 |
0.056347 |
|
|
24 |
Coastal Roadways Ltd. |
83.8 |
0.023147 |
|
|
25 |
Kabra Commercial Ltd. |
72.18 |
0.019937 |
|
|
26 |
Girish Travels & Couriers Ltd. |
42.7 |
0.011794 |
|
|
27 |
Corporate Courier & Cargo Ltd. |
31.32 |
0.008651 |
|
|
28 |
Containerway International Ltd. |
31.05 |
0.008576 |
|
|
29 |
Marine Cargo Co. Ltd. |
4.5 |
0.001243 |
|
|
Total |
362040.3 |
100 |
Selection of financial ratios
Different ratios were considered for analyzing the financial performance of the logistics sector. This paper uses 43 financial ratios grouped into four different categories. The below table defines the ratio group and ratio code provided for different ratios used in the study.
Table 1: Ratio name and code
|
Ratio group |
Ratio Code |
Ratios |
|
Profitability and earnings ratios |
PE 1 |
PBDITA as % of total income |
|
PE 2 |
PBT as % of total income |
|
|
PE 3 |
PAT as % of total income |
|
|
PE 4 |
Cash profit as % of total income |
|
|
PE 5 |
Net profit margin |
|
|
PE 6 |
Cash profit net of P&E as % of total income net of P&E |
|
|
PE 7 |
Operating profit margin of non-financial companies |
|
|
PE 8 |
Return on net worth |
|
|
PE 9 |
Return on capital employed |
|
|
PE 10 |
Return on total assets |
|
|
PE 11 |
Return on average capital employed |
|
|
PE 12 |
Return on average assets |
|
|
PE 13 |
Gross Profit Ratio |
|
|
Liquidity and cash ratios |
LC 14 |
Quick ratio (times) |
|
LC 15 |
Current ratio (times) |
|
|
LC 16 |
Cash to current liabilities (times) |
|
|
LC 17 |
Cash to average cost of sales per day |
|
|
LC 18 |
Cash flow generated from operation/ PBIT |
|
|
LC 19 |
Cash flow generated from operation to total assets |
|
|
LC 20 |
Cash flow generated from operation to capital employed |
|
|
LC 21 |
Cash flow generated from operation to net working capital |
|
|
LC 22 |
Cash flow generated from operation to average total assets |
|
|
LC 23 |
Cash flow generated from operation to Average capital employed |
|
|
LC 24 |
Cash and cash equivalent at the end to current assets |
|
|
LC 25 |
Cash and cash equivalent at the end to total assets |
|
|
LC 26 |
Cash and cash equivalent at the end to net sales |
|
|
LC 27 |
Net working capital ratio |
|
|
LC 28 |
Cash and cash equivalent at the end to current liability |
|
|
Assets management ratios |
AM 29 |
Debtors’ turnover (times) |
|
AM 30 |
Creditors turnover (times) |
|
|
AM 31 |
Gross fixed assets utilization ratio(times) |
|
|
AM 32 |
Net fixed assets utilization ratio(times) |
|
|
AM 33 |
Sales / Net fixed assets |
|
|
AM 34 |
Current Assets turnover |
|
|
AM 35 |
Working capital turnover |
|
|
AM 36 |
Total assets turnover |
|
|
AM 37 |
Sales to capital employed |
|
|
AM 38 |
Working capital to total assets |
|
|
AM 39 |
Employees utilization ratio(times) |
|
|
Solvency ratios |
S 40 |
TOL/TNW (times) |
|
S 41 |
Net Worth to Capital Employed |
|
|
S 42 |
Equity Ratio |
|
|
S 43 |
Net Fixed Assetsto capital employed |
Result and discussion
Principal component analysis was used to evaluate and analyze the financial performance (Jolliffe & Cadima 2016, Sehgal et al. 2014, Eastment&Krzanowski 1982, Wang & Du, 2000) of selected Logistics Companies in India. Principal component analysis converts the correlated data set or variables into an uncorrelated variables dataset which explains maximum variance. Principal component analysis was introduced to propose the most acceptable ratios that can be used for evaluating the financial performance of the logistics sector in India. Standardized data is used to apply Principal component analysis.
Kaiser- Meyer Olkin (KMO) statistics is used to measure the sample adequacy. It is a test to examine the strength of the partial correlation between the variables and the appropriateness of factor analysis. It ranges from 0 to 1. KMO minimum requirement of 0.5 is necessary for the sample adequacy (Yap 2013, Liu & Bai 2021, Daryanto et al.2020, Guohua & Wenxing 2020, and Han & Ren 2020) while applying PCA to financial performance analysis. This study represents the KMO measure of sample adequacy of the sample is mediocre with 0.638which is greater than 0.5, hence PCA can be applied to the data. Bartlett’s test of sphericity is a statistic used to test the null hypothesis that the correlation matrix is an identity matrix. PCA requires that the probability associated with the test must be less than the level of significance (0.05). The Bartlett’s test of sphericity χ² (903) = 6518.246, p < .05, indicated in the study shows that correlations between variables were sufficiently large for applying Principal component analysis.
Anti image correlation matrix showed the sample adequacy of each variable separately. It helps in ascertaining the correlation and is also used to know about patterns between variables. The MSA value of each variable must be greater than 0.5. If any ratio contains an MSA value below 0.5, unfortunately, that ratio should be eliminated until the overall MSA value for the remaining variables has a value > 0.5. The result of the Anti-Image correlation matrix of the 43 ratios shows that 8 variables PE7, LC17, LC18, LC21, AM30, AM35, AM39and S43 have a value less than the minimum required value of 0.5 (MSA < 0.5). Removed these ratios and calculated anti- image correlation matrix table for the remaining 35 (43-8) variables together and separately.
Table 1: KMO and Bartlett’s test
|
Statistics |
Value |
|
KMO MSA |
0.705 |
|
Bartlett’s test of Sphericity (p value) at595 degrees of freedom |
0.000 |
Table 1 shows the values of KMO and Bartlett’s test of Sphericity, both provide a base for applying PCA and allow for further interpretation for 7 companies within the time of 9 years 2014-15 to 2022-23.
Only the variables having latent roots or eigenvalue greater than 1 are considered significant and are extracted as principal components, variables having latent roots or eigenvalue less than 1 are considered insignificant and are disregarded for further analysis. Table 2 explains the total variance between ratios. All the ratios together explain 100% variation in the data set. It can be seen from the table that only 6 components, which explain 91% of the variation, have an eigenvalue greater than 1. The first Component has an eigenvalue of 8.110, the second component has a 5.125 eigenvalue, the third component has with 4.916 eigenvalues, the fourth component has an eigenvalue of 4.843, the fifth component has a 4.577 eigenvalue and the last sixth component have eigenvalue of 4.326. Other component has eigenvalue less than 1 so all other component was disregarded. The first principal component explains the highest variation in the data followed by the second and so on in descending order.
Factor Rotation
Factor rotation improves the interpretation by reducing ambiguities from the unrotated factors. Rotating the factor matrix helps in redistributing the variance from earlier factors to later ones to achieve a simpler and theoretically more meaningful factor pattern. Factor rotation is of two types Orthogonal factor rotation and Oblique factor rotation. This study used orthogonal methods of factor rotation because the main objective of using factor analysis is data reduction. There are three different approaches to orthogonal rotation, quartimax, varimax and equimax. Among these varimax rotation approach is used in the study as it is most popular and preferred.
Table 2: Total Variance Explained between Ratios
|
Component |
Initial Eigenvalues |
Rotation Sums of Squared Loadings |
||||
|
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
|
1 |
12.743 |
36.410 |
36.410 |
8.110 |
23.172 |
23.172 |
|
2 |
7.663 |
21.895 |
58.304 |
5.125 |
14.642 |
37.814 |
|
3 |
5.292 |
15.121 |
73.425 |
4.916 |
14.046 |
51.860 |
|
4 |
3.311 |
9.461 |
82.886 |
4.843 |
13.836 |
65.696 |
|
5 |
1.733 |
4.952 |
87.838 |
4.577 |
13.076 |
78.772 |
|
6 |
1.153 |
3.294 |
91.132 |
4.326 |
12.360 |
91.132 |
|
7 |
.798 |
2.281 |
93.413 |
|
|
|
|
8 |
.611 |
1.745 |
95.158 |
|
|
|
|
9 |
.412 |
1.178 |
96.336 |
|
|
|
|
10 |
.282 |
.806 |
97.142 |
|
|
|
|
11 |
.244 |
.698 |
97.841 |
|
|
|
|
12 |
.180 |
.515 |
98.356 |
|
|
|
|
13 |
.147 |
.421 |
98.777 |
|
|
|
|
14 |
.111 |
.316 |
99.093 |
|
|
|
|
15 |
.081 |
.233 |
99.326 |
|
|
|
|
16 |
.051 |
.144 |
99.470 |
|
|
|
|
17 |
.034 |
.098 |
99.568 |
|
|
|
|
18 |
.030 |
.087 |
99.655 |
|
|
|
|
19 |
.026 |
.076 |
99.731 |
|
|
|
|
20 |
.024 |
.067 |
99.798 |
|
|
|
|
21 |
.020 |
.058 |
99.856 |
|
|
|
|
22 |
.014 |
.039 |
99.894 |
|
|
|
|
23 |
.009 |
.027 |
99.921 |
|
|
|
|
24 |
.007 |
.021 |
99.942 |
|
|
|
|
25 |
.007 |
.019 |
99.961 |
|
|
|
|
26 |
.004 |
.011 |
99.972 |
|
|
|
|
27 |
.003 |
.009 |
99.982 |
|
|
|
|
28 |
.002 |
.005 |
99.987 |
|
|
|
|
29 |
.001 |
.004 |
99.991 |
|
|
|
|
30 |
.001 |
.003 |
99.994 |
|
|
|
|
31 |
.001 |
.002 |
99.996 |
|
|
|
|
32 |
.001 |
.002 |
99.997 |
|
|
|
|
33 |
.000 |
.001 |
99.999 |
|
|
|
|
34 |
.000 |
.001 |
100.000 |
|
|
|
|
35 |
6.600E-5 |
.000 |
100.000 |
|
|
|
Significance of factor loadings
Factor loading is the correlation of variable and the factor and squared of loading is the amount of the variable’s total variance accounted for by the factor. Thus a 0.30 loading translates to 10% (0.30*0.30) explanation approximately. The larger the size of the factor loading, the more important the loading in interpreting the factor matrix. In this research paper, factor loading must exceed 0.70(Hair et al., 2019) based on sample size.
Table 3: Rotated Component Matrixwith factor loading
|
|
1 |
2 |
3 |
4 |
5 |
6 |
|
PE4 |
0.889 |
|
|
|
|
|
|
PE2 |
0.887 |
|
|
|
|
|
|
PE6 |
0.884 |
|
|
|
|
|
|
PE1 |
0.881 |
|
|
|
|
|
|
PE3 |
0.870 |
|
|
|
|
|
|
PE5 |
0.858 |
|
|
|
|
|
|
PE13 |
0.719 |
|
|
|
|
|
|
AM36 |
|
|
|
|
|
|
|
S42 |
|
|
|
|
|
|
|
S41 |
|
|
|
|
|
|
|
AM34 |
|
|
|
|
|
|
|
AM31 |
|
0.922 |
|
|
|
|
|
AM32 |
|
0.916 |
|
|
|
|
|
AM33 |
|
0.906 |
|
|
|
|
|
AM37 |
|
0.765 |
|
|
|
|
|
AM29 |
|
|
|
|
|
|
|
LC14 |
|
|
0.944 |
|
|
|
|
LC15 |
|
|
0.940 |
|
|
|
|
LC27 |
|
|
0.932 |
|
|
|
|
LC16 |
|
|
0.852 |
|
|
|
|
AM38 |
|
|
0.743 |
|
|
|
|
PE12 |
|
|
|
0.958 |
|
|
|
PE10 |
|
|
|
0.896 |
|
|
|
PE11 |
|
|
|
0.826 |
|
|
|
PE8 |
|
|
|
0.781 |
|
|
|
PE9 |
|
|
|
0.772 |
|
|
|
LC26 |
|
|
|
|
0.956 |
|
|
LC24 |
|
|
|
|
0.909 |
|
|
LC28 |
|
|
|
|
0.822 |
|
|
LC25 |
|
|
|
|
0.788 |
|
|
S40 |
|
|
|
|
|
|
|
LC19 |
|
|
|
|
|
0.886 |
|
LC22 |
|
|
|
|
|
0.879 |
|
LC20 |
|
|
|
|
|
0.875 |
|
LC23 |
|
|
|
|
|
0.857 |
Extraction method: Principal component analysis, Rotation converged in 8 iterations.
The variables having insignificant factor loading (˂ ±0.7) were eliminated. Table 3 represents the mix of ratios with factor loading and communalities. In total 6 principal components were ascertained using PCA on 35 ratios. PCA reduced the set of ratios to 29 ratios and eliminated 6 ratios, represented in Table 4, which did not become part of any of the principal components, from further analysis.
Table 4: Name of the ratios eliminated
|
Ratios code |
Ratios name |
|
AM36 |
Total assets turnover |
|
S42 |
Equity Ratio |
|
S41 |
Net Worth to Capital Employed |
|
AM34 |
Current Assets turnover |
|
AM29 |
Debtors’ turnover (times) |
|
S40 |
TOL/TNW (times) |
Principal Component Analysis II
The remaining 29 ratios were analyzed again and to verify the reliability and validity of the extraction, the KMO measure of Sampling Adequacy & Bartlett’s test was done. The results of the test are shown in Table 5 as given below. In table 5 KMO value is 0.692 which means the data can be used for principal component analysis. Also, the p-value (sig.) corresponding to the chi-square value is less than 0.05 (level of significance) which leads to the rejection of the hypothesis that the correlation is insignificant.
Table 5: KMO and Bartlett’s test
|
Statistics |
Value |
|
KMO MSA |
.692 |
|
Bartlett’s test of Sphericity (p value) at 406 degrees of freedom |
0.000 |
Principal component analysis II resulted in the extraction of five principal components as provided by table 6. It can be seen that the first five components have an eigenvalue greater than 1, which are 7.786, 5.613, 4.778, 4.293, and 3.917 respectively with a cumulative variance contribution rate of 90.99%. Hence, these principal components reflect most of the information about the financial performance of the logistics sector.
Table 6: Total Variance Explained between Ratios
|
Component |
Initial Eigenvalues |
Rotation Sums of Squared Loadings |
||||
|
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
|
1 |
9.985 |
34.430 |
34.430 |
7.786 |
26.849 |
26.849 |
|
2 |
7.155 |
24.671 |
59.101 |
5.613 |
19.356 |
46.205 |
|
3 |
4.768 |
16.441 |
75.543 |
4.778 |
16.474 |
62.679 |
|
4 |
2.824 |
9.736 |
85.279 |
4.293 |
14.805 |
77.484 |
|
5 |
1.656 |
5.711 |
90.990 |
3.917 |
13.506 |
90.990 |
|
6 |
0.943 |
3.252 |
94.242 |
|
|
|
|
7 |
0.503 |
1.734 |
95.976 |
|
|
|
|
8 |
0.310 |
1.070 |
97.046 |
|
|
|
|
9 |
0.204 |
0.704 |
97.750 |
|
|
|
|
10 |
0.177 |
0.611 |
98.362 |
|
|
|
|
11 |
0.133 |
0.458 |
98.819 |
|
|
|
|
12 |
0.083 |
0.287 |
99.106 |
|
|
|
|
13 |
0.061 |
0.211 |
99.317 |
|
|
|
|
14 |
0.048 |
0.164 |
99.481 |
|
|
|
|
15 |
0.037 |
0.129 |
99.610 |
|
|
|
|
16 |
0.030 |
0.102 |
99.712 |
|
|
|
|
17 |
0.022 |
0.076 |
99.788 |
|
|
|
|
18 |
0.018 |
0.061 |
99.849 |
|
|
|
|
19 |
0.013 |
0.044 |
99.893 |
|
|
|
|
20 |
0.010 |
0.033 |
99.926 |
|
|
|
|
21 |
0.008 |
0.029 |
99.955 |
|
|
|
|
22 |
0.005 |
0.016 |
99.971 |
|
|
|
|
23 |
0.003 |
0.011 |
99.981 |
|
|
|
|
24 |
0.002 |
0.006 |
99.987 |
|
|
|
|
25 |
0.001 |
0.005 |
99.992 |
|
|
|
|
26 |
0.001 |
0.004 |
99.996 |
|
|
|
|
27 |
0.001 |
0.002 |
99.998 |
|
|
|
|
28 |
0.001 |
0.002 |
100.000 |
|
|
|
|
29 |
0.000 |
0.000 |
100.000 |
|
|
|
Each Variable was allotted to the component where it had maximum factor loading. The details of the factors, its constituents’ratios,and factor loadingare given in table 7.
Table 7: Factors, its constituents’ ratios, and factor loading.
|
|
PC1 |
PC2 |
PC3 |
PC4 |
PC5 |
|
PE1 |
0.924 |
|
|
|
|
|
PE4 |
0.905 |
|
|
|
|
|
PE2 |
0.892 |
|
|
|
|
|
PE3 |
0.887 |
|
|
|
|
|
PE6 |
0.872 |
|
|
|
|
|
PE5 |
0.827 |
|
|
|
|
|
AM37 |
-0.791 |
|
|
|
|
|
PE13 |
|
|
|
|
|
|
AM31 |
|
|
|
|
|
|
AM32 |
|
|
|
|
|
|
AM33 |
|
|
|
|
|
|
PE11 |
|
0.875 |
|
|
|
|
PE9 |
|
0.863 |
|
|
|
|
PE10 |
|
0.844 |
|
|
|
|
PE12 |
|
0.833 |
|
|
|
|
PE8 |
|
0.770 |
|
|
|
|
LC14 |
|
|
0.954 |
|
|
|
LC15 |
|
|
0.950 |
|
|
|
LC27 |
|
|
0.943 |
|
|
|
LC16 |
|
|
0.898 |
|
|
|
AM38 |
|
|
0.735 |
|
|
|
LC19 |
|
|
|
0.918 |
|
|
LC22 |
|
|
|
0.916 |
|
|
LC20 |
|
|
|
0.834 |
|
|
LC23 |
|
|
|
0.812 |
|
|
LC26 |
|
|
|
|
0.969 |
|
LC24 |
|
|
|
|
0.925 |
|
LC28 |
|
|
|
|
0.870 |
|
LC25 |
|
|
|
|
0.806 |
Hence performance of logistics industry is measured mainly with these 5 classes of ratios.But again 4 ratios were deleted from the further analysis as they did not fall in any of the principal component. Table 8 provide the name of the ratios which were deleted from the analysis.
Table 8: Name of the ratios eliminated
|
Ratios code |
Ratios name |
|
PE13 |
Gross Profit Ratio |
|
AM31 |
Gross fixed assets utilization ratio(times) |
|
AM32 |
Net fixed assets utilization ratio(times) |
|
AM33 |
Sales / Net fixed assets |
Principal Component III
Post the second Factor Analysis the data was deemed to be fit for final processing. Factor Analysis was again carried out with the intention of classifying the ratios into valid groups. The Analysis on the remaining 25 ratios (29- 4) led to five factors which account for 93.424% variation. Further, KMO value was 0.649, more than 0.5, and Bartlett's test is statistically significant, tabulated in Table 9.
Table 9: KMO and Bartlett’s test
|
Statistics |
Value |
|
KMO MSA |
.649 |
|
Bartlett’s test of Sphericity (p value) at 300 degrees of freedom |
0.000 |
Principal component analysis III resulted in extraction of five principal components as provided by table 10. It can be seen that first five components have an eigenvalue greater than 1, which are 6.354, 5.066, 4.428, 3.777 and 3.731 respectively with cumulative variance contribution rate of 93.42%. Hence, these principal components reflect most of the information about financial performance of the logistics sector.
Table 10: Total Variance Explained between Ratios
|
Component |
Initial Eigenvalues |
Rotation Sums of Squared Loadings |
||||
|
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
|
1 |
8.935 |
35.739 |
35.739 |
6.354 |
25.417 |
25.417 |
|
2 |
6.901 |
27.605 |
63.344 |
5.066 |
20.263 |
45.679 |
|
3 |
3.945 |
15.779 |
79.123 |
4.428 |
17.713 |
63.393 |
|
4 |
1.937 |
7.748 |
86.871 |
3.777 |
15.108 |
78.500 |
|
5 |
1.638 |
6.553 |
93.424 |
3.731 |
14.923 |
93.424 |
|
6 |
0.475 |
1.898 |
95.322 |
|
|
|
|
7 |
0.380 |
1.520 |
96.842 |
|
|
|
|
8 |
0.229 |
0.914 |
97.756 |
|
|
|
|
9 |
0.151 |
0.605 |
98.361 |
|
|
|
|
10 |
0.116 |
0.464 |
98.826 |
|
|
|
|
11 |
0.068 |
0.273 |
99.099 |
|
|
|
|
12 |
0.061 |
0.243 |
99.342 |
|
|
|
|
13 |
0.038 |
0.154 |
99.496 |
|
|
|
|
14 |
0.032 |
0.128 |
99.624 |
|
|
|
|
15 |
0.027 |
0.109 |
99.733 |
|
|
|
|
16 |
0.019 |
0.077 |
99.811 |
|
|
|
|
17 |
0.015 |
0.058 |
99.869 |
|
|
|
|
18 |
0.011 |
0.045 |
99.914 |
|
|
|
|
19 |
0.010 |
0.038 |
99.952 |
|
|
|
|
20 |
0.005 |
0.021 |
99.973 |
|
|
|
|
21 |
0.004 |
0.015 |
99.988 |
|
|
|
|
22 |
0.002 |
0.007 |
99.994 |
|
|
|
|
23 |
0.001 |
0.003 |
99.997 |
|
|
|
|
24 |
0.001 |
0.002 |
99.999 |
|
|
|
|
25 |
0.000 |
0.001 |
100.000 |
|
|
|
Extraction Method: Principal Component Analysis.Rotation converged in 7 iterations.
Accordingly, components names were assigned based on the highest factor loading of the related variables. The findings of the components post the third -principal component analysis, along with the name of components, representative ratio, factor loading, are collated in Table 11.
Table 11: Components, its constituents’ ratios with loading.
|
|
PC1 |
PC2 |
PC3 |
PC4 |
PC5 |
|
PE1 |
0.951 |
|
|
|
|
|
PE4 |
0.929 |
|
|
|
|
|
PE2 |
0.921 |
|
|
|
|
|
PE3 |
0.919 |
|
|
|
|
|
PE6 |
0.896 |
|
|
|
|
|
PE5 |
0.860 |
|
|
|
|
|
AM37 |
-0.754 |
|
|
|
|
|
PE12 |
|
0.922 |
|
|
|
|
PE10 |
|
0.916 |
|
|
|
|
PE11 |
|
0.908 |
|
|
|
|
PE9 |
|
0.888 |
|
|
|
|
PE8 |
|
0.830 |
|
|
|
|
LC14 |
|
|
0.950 |
|
|
|
LC15 |
|
|
0.947 |
|
|
|
LC27 |
|
|
0.940 |
|
|
|
LC16 |
|
|
0.884 |
|
|
|
AM38 |
|
|
0.753 |
|
|
|
LC26 |
|
|
|
0.973 |
|
|
LC24 |
|
|
|
0.915 |
|
|
LC28 |
|
|
|
0.860 |
|
|
LC25 |
|
|
|
0.808 |
|
|
LC19 |
|
|
|
|
0.928 |
|
LC22 |
|
|
|
|
0.921 |
|
LC20 |
|
|
|
|
0.866 |
|
LC23 |
|
|
|
|
0.841 |
Extracted Principal components and related ratio are presented in figure 1 which represent that 7 ratios become the part of 1st component, 5 ratios fall under second component, 5 in 3rd component, 4 ratios in 4th component and remaining 4 ratios in component 5.
Figure 1: Extracted Principal components and related ratio
Table 12 represents the mix of important ratios which significantly affects the performance of logistics industry under consideration for a period of 9 years from 2014-15 to 2022-23. Financial performance of Logistics sector in India is measured through mainly by five classes of ratios.
Table 12: Principal Component with its name
|
Principal component |
Component name based on the group of ratios |
|
PC1 |
Profitability Margin Ratios |
|
PC2 |
Profitability Return Ratios |
|
PC3 |
Liquidity Ratio |
|
PC4 |
Cash and Cash Equivalent Ratios |
|
PC5 |
Cash Flow from Operation Ratio |
Profitability margin ratios being the first principal component is very important. Margin ratios provide understandings into a firm’s capability to generate profit from sales and the efficiency of its sales process. Most of the margin ratio have value greater than 0.8, very significant value. These ratio shows earning of the business after tax and before tax expenses. It shows that the logistics sector converts the sales in profit very effectively.
Profitability return ratio plays a very effective role in improving the performance of Indian logistics sector. This is supported by most of the literatures as maximisation of profit should be the core aim of the business to provide high return to shareholders. These ratios reveal your business’s capacity to produce returns on investment based on the net worth, capital employed, and total assets your business has. Most of the return ratios have a very significant value of 0.9.
Liquidity in the logistics industry is very important as proved by component 3. All liquidity ratio has very noteworthy value of greater than 0.9. Liquidity is required to pay off its current liabilities. It shows the efficiency of the business to convert the current assets into cash and handling the operation of markets.
Cash and cash equivalent the most liquid current assets and used in growth and expansion of operations. According to PCA these ratios are less significant as they become the part of 4th component.
Operating cash flow become the part of 5th component which signifies its importance in logistics sector. Cash from operation indicates the amount of money a business brings in from its regular business activities of providing services to customers, utilising its total assets and total capital employed. It helps in ascertaining the financial success of a companies’ core business.
Factor score
Factor score represents the transformed values of the original values onto the new orthogonal axes obtained through the principal component analysis. Factor scores are standardised score with mean zero and standard deviation as 1. Further analysis can be carried out on the factor scores rather than the original data. Finally, this study applies Anderson – Rubin method for calculating the factor score (Field, 2009: 635). The result of factor score is represented intable13.
Assignments of weights to Principal components
To achieve the last and third objective of the study of ranking to different companies based on these principal components, assigned weights to the principal components. The variance contribution rate or eigenvalue of each six principal components is divided by the cumulative contribution rate of all six principal components, and this quotient was used as weight. (Liu & Bai, (2021), Han & Ren, (2020), Guohua & Wenxing, (2020))
W=
Table 13: Calculation of weights for each principal component
|
Principal components |
Total eigen value |
Weights assigned to each Principal component |
|
PC 1 |
6.354 |
6.354/23.356=0.272 |
|
PC 2 |
5.066 |
5.066/23.356=0.217 |
|
PC 3 |
4.428 |
4.428/23.356=0.190 |
|
PC 4 |
3.777 |
3.777/23.356=0.162 |
|
PC 5 |
3.731 |
3.731/23.356=0.160 |
|
Total |
23.356 |
|
Using these weights, comprehensive score of financial performance of each and every company for nine years from 2014-15 to 2022-23 were calculated by using the following equation:
Financial performance = 0.272 PC1+ 0.217PC2+0.190PC3+0.162PC4+0.160PC5
Table 14: Average comprehensive score and ranking
|
Company name |
Year |
PC1 |
PC2 |
PC3 |
PC4 |
PC5 |
Comprehensive score |
Average Comprehensive score |
Ranking |
|
Blue Dart Express Ltd.
|
2014-15 |
0.045 |
0.792 |
-0.067 |
2.205 |
0.702 |
0.639952 |
0.41433
|
2nd |
|
2015-16 |
0.703 |
1.357 |
0.071 |
3.846 |
1.390 |
1.342849 |
|||
|
2016-17 |
0.220 |
0.389 |
-0.548 |
2.841 |
-0.066 |
0.489122 |
|||
|
2017-18 |
-0.025 |
0.293 |
-0.290 |
2.230 |
0.633 |
0.4634 |
|||
|
2018-19 |
-0.517 |
-0.552 |
-0.242 |
2.464 |
0.164 |
0.118481 |
|||
|
2019-20 |
-1.124 |
-1.578 |
-0.393 |
0.336 |
0.076 |
-0.65597 |
|||
|
2020-21 |
-0.486 |
-0.719 |
-0.060 |
-0.344 |
2.144 |
-0.01244 |
|||
|
2021-22 |
0.517 |
2.182 |
-0.359 |
0.235 |
2.396 |
0.966506 |
|||
|
2022-23 |
0.110 |
2.116 |
-0.518 |
-0.578 |
0.500 |
0.377072 |
|||
|
Allcargo Logistics Ltd.
|
2014-15 |
1.114 |
-0.412 |
-0.664 |
-0.166 |
0.027 |
0.065204 |
-0.03074
|
4th |
|
2015-16 |
1.135 |
-0.127 |
-0.781 |
-0.405 |
0.065 |
0.078123 |
|||
|
2016-17 |
1.069 |
-0.231 |
-0.774 |
-0.403 |
-0.205 |
-0.00404 |
|||
|
2017-18 |
0.113 |
-0.998 |
-0.318 |
-0.525 |
-0.006 |
-0.33197 |
|||
|
2018-19 |
1.315 |
0.995 |
-0.860 |
-0.654 |
-0.845 |
0.169827 |
|||
|
2019-20 |
1.099 |
-0.252 |
-1.028 |
-0.071 |
-0.553 |
-0.05069 |
|||
|
2020-21 |
0.901 |
-0.001 |
-1.157 |
-0.246 |
-0.705 |
-0.12676 |
|||
|
2021-22 |
0.610 |
1.073 |
-0.943 |
-0.282 |
-1.458 |
-0.05849 |
|||
|
2022-23 |
-0.206 |
0.024 |
-0.082 |
-0.949 |
1.265 |
-0.01787 |
|||
|
Transport Corporation Of India Ltd.
|
2014-15 |
-0.752 |
0.366 |
-0.096 |
-1.106 |
0.375 |
-0.26233 |
0.034396
|
3rd |
|
2015-16 |
-0.506 |
0.226 |
-0.397 |
-1.053 |
0.093 |
-0.31941 |
|||
|
2016-17 |
-0.464 |
0.137 |
-0.331 |
-0.817 |
-0.203 |
-0.32396 |
|||
|
2017-18 |
-0.402 |
0.543 |
-0.176 |
-1.182 |
0.423 |
-0.14846 |
|||
|
2018-19 |
-0.448 |
0.708 |
-0.141 |
-1.336 |
0.229 |
-0.17449 |
|||
|
2019-20 |
-0.465 |
0.413 |
0.008 |
-1.328 |
0.833 |
-0.11738 |
|||
|
2020-21 |
-0.379 |
0.336 |
0.278 |
-1.089 |
1.006 |
0.007037 |
|||
|
2021-22 |
0.135 |
1.883 |
1.016 |
-0.998 |
1.382 |
0.697279 |
|||
|
2022-23 |
0.169 |
1.832 |
1.662 |
0.651 |
0.550 |
0.951287 |
|||
|
Sindhu Trade Links Ltd. (1992)
|
2014-15 |
0.435 |
-0.329 |
-0.887 |
-0.573 |
1.317 |
-0.00345 |
-0.16055
|
5th |
|
2015-16 |
1.209 |
1.614 |
-0.737 |
-0.412 |
-1.616 |
0.214354 |
|||
|
2016-17 |
0.503 |
-0.055 |
-0.749 |
-0.457 |
1.275 |
0.112798 |
|||
|
2017-18 |
0.260 |
-0.180 |
-0.863 |
-0.781 |
1.000 |
-0.09838 |
|||
|
2018-19 |
0.267 |
-0.263 |
-0.863 |
-0.445 |
0.662 |
-0.11401 |
|||
|
2019-20 |
0.661 |
-0.709 |
-1.162 |
-0.293 |
0.754 |
-0.12147 |
|||
|
2020-21 |
0.347 |
-0.498 |
-1.329 |
0.225 |
-2.761 |
-0.67039 |
|||
|
2021-22 |
-0.134 |
-0.774 |
-0.330 |
-0.754 |
-0.052 |
-0.39717 |
|||
|
2022-23 |
0.405 |
-0.632 |
-0.335 |
-0.372 |
-1.357 |
-0.36727 |
|||
|
Mahindra Logistics Ltd.
|
2014-15 |
-1.648 |
1.435 |
0.944 |
1.287 |
-0.798 |
0.12262 |
-0.28628
|
6th |
|
2015-16 |
-1.637 |
1.259 |
0.645 |
1.004 |
-2.845 |
-0.34226 |
|||
|
2016-17 |
-1.768 |
1.227 |
0.388 |
-0.477 |
-1.630 |
-0.47898 |
|||
|
2017-18 |
-1.754 |
1.227 |
0.499 |
-0.476 |
-0.718 |
-0.30814 |
|||
|
2018-19 |
-1.710 |
1.375 |
0.546 |
-0.942 |
0.164 |
-0.18972 |
|||
|
2019-20 |
-1.233 |
-0.002 |
0.087 |
0.048 |
-0.262 |
-0.3535 |
|||
|
2020-21 |
-1.095 |
-1.328 |
0.167 |
1.234 |
0.964 |
-0.2006 |
|||
|
2021-22 |
-1.155 |
-1.287 |
-0.014 |
0.185 |
1.096 |
-0.39115 |
|||
|
2022-23 |
-0.969 |
-0.623 |
-0.280 |
-0.080 |
0.187 |
-0.43481 |
|||
|
Reliance Industrial Infrastructure Ltd.
|
2014-15 |
2.881 |
0.078 |
1.045 |
0.288 |
0.325 |
1.097263 |
0.424144
|
1st |
|
2015-16 |
1.797 |
-0.301 |
1.512 |
-0.017 |
-0.367 |
0.648962 |
|||
|
2016-17 |
2.120 |
-0.450 |
-0.203 |
0.516 |
-0.271 |
0.480852 |
|||
|
2017-18 |
1.256 |
-0.739 |
-0.339 |
0.081 |
-0.705 |
0.017735 |
|||
|
2018-19 |
0.779 |
-0.936 |
0.386 |
-0.185 |
-0.443 |
-0.0188 |
|||
|
2019-20 |
0.751 |
-0.858 |
0.375 |
0.176 |
-1.140 |
-0.06432 |
|||
|
2020-21 |
1.113 |
-0.842 |
1.228 |
-0.057 |
-1.177 |
0.155753 |
|||
|
2021-22 |
0.195 |
-1.243 |
4.654 |
-0.449 |
-0.118 |
0.574231 |
|||
|
2022-23 |
1.326 |
-0.427 |
3.871 |
-0.552 |
0.080 |
0.925616 |
|||
|
ShreejiTranslogistics Ltd.
|
2014-15 |
-0.928 |
-1.651 |
-0.227 |
0.157 |
0.393 |
-0.56525 |
-0.39529
|
7th |
|
2015-16 |
-0.846 |
-1.505 |
-0.113 |
0.420 |
1.074 |
-0.33832 |
|||
|
2016-17 |
-0.983 |
-1.065 |
-0.169 |
-0.401 |
0.030 |
-0.59039 |
|||
|
2017-18 |
-0.608 |
-0.296 |
-0.264 |
0.309 |
-1.116 |
-0.40779 |
|||
|
2018-19 |
-0.575 |
-0.355 |
-0.112 |
0.185 |
-0.251 |
-0.2648 |
|||
|
2019-20 |
-0.890 |
-1.274 |
-0.128 |
0.003 |
-0.473 |
-0.61777 |
|||
|
2020-21 |
-1.179 |
-2.009 |
-0.056 |
0.706 |
-0.469 |
-0.72802 |
|||
|
2021-22 |
-0.334 |
0.794 |
0.055 |
-0.302 |
-0.670 |
-0.06422 |
|||
|
2022-23 |
-0.337 |
0.824 |
-0.052 |
-0.072 |
-0.293 |
0.018951 |
Evaluation of financial performance of logistics sector based on Principal component analysis
In this paper, the ranking of 7 Indian listed Logistics companies is obtained using PCA (calculated by SPSS) and Average comprehensive score (calculated by Excel) and presented in Table 13.
Reliance Industrial Infrastructure Ltd. captured the first rank with an average comprehensive score of 0.424 and Shreeji Trans Logistics Ltd. ranked last with a negative comprehensive score of -0.395. The difference between the largest value and lowest value is relatively huge which shows a relative difference in the financial performance of logistics companies. Among all seven companies under consideration, only the top 3 companies have a positive average comprehensive score and the remaining 4 have a negative score. Although some listed companies are at the top of the average comprehensive ranking, there are still many areas that need to be improved. Reliance Industrial Infrastructure Ltd. still ranked first in profitability return ratio and cash aloe from operation ratio needs great improvement but because of the highest profitability margin ratio as the first component achieved the first rank.
Blue Dart Express Ltd. ranked second because of unhealthy profit margins and liquidity problems. The company has the highest score of Cash and Cash Equivalent Ratios with Cash Flow from Operation Ratio. Transport Corporation of India Ltd. faces the problem of profitability margin which resulted in low cash and cash equivalent. The remaining companies have negative average comprehensive scores because of low profitability, liquidity problems, etc. Mahindra Logistics Ltd. ranks 6th in the comprehensive score but has an advantage in liquidity and profitability return. Allcargo Logistics Ltd. is in 4th rank but has an advantage in profitability margins.
In terms of profitability margin 3 companies have positive scores and the remaining 4 have negative scores in the period under consideration of 9 years from 2014-15 to 2022-23. The First is Reliance Industrial Infrastructure Ltd. and the last is Mahindra Logistics Ltd. indicates the difference in the profitability margin of different companies in the logistics sector.
In terms of profitability return, more than 50% of companies score positive, which indicates that the logistics sector has good profitability return potential and shows the business’s capacity to produce returns on investment based on the total assets, capital employed, and net worth of your business.
In terms of liquidity, more than 50% of companies score negatively showing that the short-term solvency of the logistics sector in India is not strong. It shows the inefficiency of the business in converting the current assets into cash and handling the operation of markets.
Cash and cash equivalent and Cash flow generated from the operation situation of the logistics sector are inefficient as more than 50% of companies have negative scores in 9 years under consideration. Companies face a problem in the growth and expansion of operations. It hampers the financial success of a company’s core business.
Conclusion
To make a comprehensive evaluation of the financial performance of selected Indian-listed logistics companies through principal component analysis on SPSS, this study selects 43 financial indicators. The study resulted in an imbalance in the performance of the logistics sector with a significant gap in a comprehensive score of 7 selected companies. some companies ranked high and some low showing the inability of some companies to cope with the other companies in the sector. Managers of logistics companies can frame strategic policies using their comprehensive evaluation of financial performance and improve the performance as well as other users of financial statements can use this score in judging the performance.
Scope for further studies
More companies with higher time duration can benefit the government and industry in drafting the policies to improve the sector performance. Comprehensive scores through PCA or another model can be used in another sector also.
References
