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Volume:01, Number:05, Sep-2011 Page 97

www.theinternationaljournal.org

PERFORMANCE EVALUATION OF INDIAN APPAREL

INDUSTRY USING FINANCIAL RATIOS

Shreeshail M. Pharsiyawar,Assistant Professor,Department of Industrial & Poduction Engineering,Basaveshwar Engineering College,Bagalkot (India)

Dr. Umesh M Bhushi,Principal,Sahyadri College of Engineering & Management,Mangalore (India)

Dr. Channappa M. Javalagi,Professor,Department of Industrial & Poduction Engineering,Basaveshwar Engineering College,Bagalkot (India)

Abstract

Productivity measurement is one of the methods to assess the performance of industries.There are several ways to measure the productivity and the adoption depends on the objective

and orientation of the results. Company financial ratios are better predictors of theproductivity of an industry. In this paper, we have proposed a performance evaluationmodel to assess productivity of the Indian Apparel industry using firm level data. Financialratios of apparel firms for a period of 15 years have been collected grouped under 4 majorcategories. To select better performance indices, with minimal variables, to explain theoverall performance of the firms, Factor Analysis-Principal Component Analysis (PCA) has

been used. The contributing factors have been identified based on the results. These results ofbusiness performance evaluation are useful for the middle or top level managers in theprocess of business decision making.

Index TermsApparel, Productivity, Financial ratio, Principal Component Analysis

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INTRODUCTION

Indian Textile Industry flourished in ancient times and was an important industry from 15th

to 19

th

century[1]. The global textile and clothing industry occupies an important position inthe total volume of merchandise trade across countries. Developing countries account forlittle over two-third of world exports in textiles and clothing [2]. A study byPricewaterhouseCoopers (2006) reports that India has the potential to be the fastest growinglarge economy in world, and is expected grow between 58% and 100% of the size of the USeconomy, which would make India clearly the third largest economy in the world by 2050 interms of market exchange rates [3],

Textile and apparel sector being second largest industry after agriculture, plays a veryimportant role in the Indian economy in terms of its share in employment, value added andexport earnings. As per the annual report of Ministry of Textiles [4], this sector accounts for

14% of industrial production, employs 35 million people and has about 12% share of the totalexports. According to the Confederation of Indian Textile Industry (CITI), the Indian textileindustry has the potential to reach US$ 110 billion by 2012 [5].

The textile sector in India consists of the subsectors like organized cotton/man-made fibretextiles mill Industry, and unorganized/ fragmented/ non-integrated man-made fibre/filament yarn industry, the wool and woolen textile industry, the sericulture and silk textileindustry, handlooms, handicrafts, the jute and jute textile industry, and textiles exports. Tothis diversified sector having complex structure, the apparel manufacturing subsectorcontributes significantly.

The number of units involved in wearing apparel manufacturing are estimated at 17.55lakh.employing around 5 million workers, out of which around 2.5 million are employed in theexport sector. As per the Textiles Committee study reports, 5lakh trained workers including4.5lakh operators, 0.22lakh jobbers, 0.11lakh pattern makers, 0.11lakh technicians/qualitycontrollers and 0.06lakh managers would be required for apparel industry[6].

Apparel and cotton textile products together contribute nearly 72% of the total textileexports. The Indian apparel industry is an export intensive industry and about one third of itstotal production is exported, accounting for almost 42% of the total textile exports. Exports ofapparel goods to major countries from India has increased from Rs. 5,03,896 millionin the year 2008-09 to Rs. 5,08,319 million in 2009-10, where as it was Rs. 4,02,803

million in the year 2006-07 [4].

In spite of the threat by the Chinese textile and industry, India has the potential to increaseits export share in world trade. High growth of Indian exports is possible due to the increasedsourcing shift from developed countries to Asia, and Indias strengths as a suitable alternativeto China for global buyers.

Apparel is the most attractive product category amongst retail product categories in termsof Returns on Capital Employed. Garmenting & Technical Textiles are the most attractivesegments within the Apparel value chain in terms of return on assets.

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Hence, in order to see that there is increased volume of production and exports, it is now,necessary for the Indian apparel industry to look back and find out the strengths andweaknesses in order to make use of the global trade opportunities, while overcoming thethreats by the countries like China. However, Indian apparel sector is labour, raw material,

and capital intensive. The firms need to reengineer their organisational resources in such away that it would be possible to increase the volume of output, while minimising the cost ofproduction. In other words, it is inevitable for the policy makers to reorient their objectivestowards enhancement of their organisational productivity.

BACKGROUNDOFTHESTUDY

Various indicators such as productivity, profitability, stability, quality or the sociability areused to quantify the efficiency of the industries/firms. The mangers major concern in anyindustry is to improve the productivity and performance. One of the ways to evaluate the

productivity is to find the financial stability and soundness, that helps in knowing the

productivity level of the firm. This could be done by analysing the financial position in termsof the firms financial ratios of the firm in comparison with the industry averages or bycomparing with the firms own past performance. Efficient management of influencingfinancial factors is the key element for upgrading a firms productivity.

Since Indian apparel industry is labour, raw material and capital oriented as discussed inthe previous section, it is necessary for every apparel firm to find the ways to minimise thecost associated with these resources. An attempt by the manager is needed, to find out thefactors that influence financial condition of the firm.

LITERATURE REVIEW

With the available literature it is evident that, various authors have empahasised on theimportance and methods of measuring the productivity, especially using the firmsaccounting financial ratios.

Grossman (19930 [7], the companies need to realize that gains in productivity are one oftheir major weapons to achieve cost and quality advantages over their competitors. Accordingto Wazed and Shamsuddin (2008) [8], productivity measurement is essential to benchmarkand improve a company performance.

Financial ratios have been widely used by researchers to assess the productivity of industry.

Most of them [9,10,11,13,14,15] have focused mainly on bankruptcy prediction andperformance evaluation of firms.

Shaw et al [16] state that, the financial ratios play an important role in financial forecastingand there has been much controversy over the appropriate classification of the financialratios. In order to make effective use of financial ratios, it is necessary to classify the largenumber of possible ratios into meaningful groups. A ratio which is a surrogate of variable,and good representative of the group of variables, can very well be able to explain a

particular dimension of the firm.

Personal Quality and productivity are important tools for organizations to use and toachieve competitive advantage, according to Marilyn M. Helms (1996) [17].

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The firms purpose is not only internal control but also better understanding of what capital

suppliers seek in financial condition and performance from it. Analysis and interpretation ofvarious ratios should give analysts a better understanding of the financial condition and

performance of the firm than they would obtain from analysis of the financial data alone [18].Since a firms management performance can be evaluated in terms of financial ratios,efficient management of the firm with the help of financial factors has been suggested byChen et al (2001)[19].

Different sets of financial ratios have been used by researchers and the industry, to assessthe firm level productivity. Jui-Ching Huang et al [20] use the ratios grouped under activity,

growth, stability, productivity, and profitability ratios.

Most common classification of these ratios consists of four groups: liquidity ratios,profitability ratio, financial leverage ratios and returns and market ratios. The CMIE (Centre

for Monitoring Indian Economy) Prowess database classification is almost similar tocommon classification and has grouped the 87 financial ratios into four groups as

Profitability ratios, Liquidity ratios, Asset utilisation ratios and Inventory/Working capital

cycle.

Chen et al [16] uses Total Factor Productivity measurement of the firm along with PrincipalComponent Analysis of the financial ratios as variables, to arrive at the factors to investigate

productivity of large scale manufacturing firms in Taiwan.

Productivity measurement using financial ratios requires the firm level data to be collectedfrom balance sheets and the cash flow statements. Based on this, the comparison and policy

decisions are made on the area to be improved in order that, the cost minimization and hencethe improvement of productivity is possible.

OBJECTIVES OF RESEARCH

The overall objective of the paper is to analyze the productivity of Indian apparel industry,using firm level financial data. The paper identifies underlying structure of the financialvariables and factors that have positive/negative impact on productivity of the firms andhighlights the major drivers of productivity growth. The outcome of this could be used bySystem Dynamics modelling to propose some of the policy interventions that are required, forsustainable and positive productivity growth. However, the current paper does not

encapsulate SD analysis.

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RESEARCH METHODOLOGY

Data related to Indian apparel industry were collected from Prowess database of Centre for

Monitoring Indian Economy (CMIE) for fifteen financial years (1993-94 to 2007-08). Thedata consists of the accounting figures of production, inventory, sales, import and export,expenditure and around 87 financial ratios. Prowess classifies these financial ratios under 4groups: viz. Profitability ratios, Liquidity ratios, Asset utilisation ratios andInventory/Working capital cycle. SPSS 19.0 software was used for study. A total of 1392observations for the 15 years period, and financial ratios as variables were used for furtheranalysis. Principal Component Analysis (PCA) was used to reduce the large number ofvariables into fewer representative factors, which could explain the influence on the overall

performance of the firm.

RESULTS AND ANALYSIS

Selection of variables

The list of variables in the original dataset with the abbreviations used is given inAppendix-1. After coding and screening the data thoroughly to detect the anomalies, thedataset was brought to normality. Appropriate financial ratios that are fit for further analysisto find out the underlying constructs were selected by removing those ratios having similarmeaning and purpose, and have almost the same values. A correlation matrix was obtained tofind the higher correlation between the variables, and the other variables with lowercorrelations were removed from the dataset. Thus, the number of variables reduced to 35,which have been marked with (#) in the Appendix-1.

Principal Component Analysis(PCA)

A initial factor extraction was carried out, and Kaisers criterion was used to select onlyfactors with an eigenvalue greater than one. The results are shown Table 1.

The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partialcorrelations among variables are small. Bartlett's test of sphericity tests whether thecorrelation matrix is an identity matrix, which indicates that the factor model is inappropriate[Hair et al, 2007] [21]. The KMO value of .897 was reported to be well above the cutoffvalue of .50, and thus it is confirmed that the factors extracted account for substantial amountof Variance.

As most of the variance can be explained by factors with high eigenvalues, this allows thenumber of factors to be greatly reduced. Those variables with the communalities less than .7were omitted from further analysis. The communalities of the variables are given in Table 2.

Table 1. KMO and Bartlett's Test Results

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .879

Bartlett's Test of Sphericity

Approx. Chi-Square 35570.778

Df 496

Sig. .000

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Table 2. Extraction

Communalities

Variable Initial Extraction

PatnNsale 1 0.65

OcafNsale 1 0.68PbitnTass 1 0.78

PatnGfa 1 0.62

PatnNw 1 0.73

PbitnCape 1 0.65

DivPat 1 0.89

EdivPat 1 0.89

RpPat 1 0.86

RpGfa 1 0.71

RpWcap 1 0.76

RpCape 1 0.73

RpTdbt 1 0.68

RpTass 1 0.79RpNw 1 0.77

DivNw 1 0.75

CaCl 1 0.69

CaAdcos 1 0.66

OcafWcap 1 0.75

Quick 1 0.74

Current 1 0.78

Capgear 1 0.96

DbtEq 1 0.96

WcapSale 1 0.73

WcapBor 1 0.70

WcapTass 1 0.81

PbitInt 1 0.51

CashprInt 1 0.78

OcafInt 1 0.78

VopCapei 1 0.87

VopTassi 1 0.83

VopNfai 1 0.78

Extraction Method: PrincipalComponent Analysis.

The sample size and the data are adequate for further analysis and the PCA holds good, as

the initial and rotation sums of squared loadings explain more than 76 % of the total varianceas shown in Table 3 next page. The total variance of 60% explained by the factors in socialsciences is considered to be satisfactory (Hair et al).

The Scree test criterion was also used as shown in the Fig. 1, for verification andconfirmation of number of factors to be retained.

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Table 3. Total Variance Explained by the Factors

Component Initial EigenvaluesExtraction Sums ofSquared Loadings

Rotation Sums ofSquared Loadings

TotalCumulative

%Total

Cumulative

%Total

Cumulative

%1 Retained Profit 3.489 38.200 3.489 38.200 3.384 33.052

2 Working Capital 2.737 49.147 2.737 49.147 2.483 42.984

3 Dividend (DIVID) 2.156 57.771 2.156 57.771 2.384 52.519

4 Asset Turnover (ASTUR) 1.719 64.645 1.719 64.645 2.308 61.750

5 Operating Cash Flow (OCAFSH) 1.614 71.103 1.614 71.103 2.038 69.902

6 Debt to Equity (DEBEQ) 1.441 67.76 1.441 76.868 1.742 76.868

Figure 1. Scree Plot for factor selectionThe Varimax method of rotation, setting a minimum loading of 0.4 was used to accept a

variable onto a factor. The rotated component matrix is with the factor loadings, removingall lower values for clarity, in Table 4 in the next page. Finally, 6 factors which confirmed tothe statistical tests, were retained and were named based on the variable having highestloading on that factor.

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Table 5. Results of unifactiriality and reliability test

FactorNo.

Factor Name Croncahs

Alpha

Total variance

explained

1 Retained Profit .923 68.804

2 WorkingCapital

.871 66.334

3 IncrementalAssetsTurnover

.897 82.946

4 Dividend .853 77.273

5 Operatingcashflow

.838 99.035

6 Debt to equity .990 84.382

The Cronbachs alphatest was used to check the reliability of the factors obtained from thePCA. Factors with alpha value greater than 0.7 are adequate in exploratory research. Table 5

shows the results of reliability test, and all the factors have alpha values above the desiredcutoff. Construct validity was checked by carrying out principal components factor analysisfor each factor. This ensured the unifactoriality of all the factors. Discriminant validity isassured, since the rotated component matrix shows that there are no cross loadings ofvariables on more than one factor or multiple factors.

In addition to the above, the split sample test was conducted by dividing the samples intotwo groups (group 1: sample no. 1-700 and group 2: sample no. 701-1392). Again the PCAwas carried out and the steps mentioned earlier were repeated for both the sample groupsseparately. The results were similar to those obtained for original set of 1392 samples. Thereliability and validity tests conducted show that, the results obtained for both the groups arewithin the specified ranges of cutoff values. Hence, the tests carried and the factors arrived at,are considered out are valid.

Table 4. Rotated Component Matrix with factor loading and corresponding name assigned to the factor *

FactorNumber

Factor Name VariablesComponent

1 2 3 4 5 6

1 Retained Profit

RpTass 0.886

RpCape 0.874

RpNw 0.812RpTdbt 0.808

RpGfa 0.825

RpWcap 0.784

RpPat 0.772

2 Working Capital

WcapTass 0.877

Current 0.868

WcapSale 0.828

WcapBor 0.745

Quick 0.740

4 Incremental Assets Turnover

VopCapei 0.921

VopTassi 0.919

VopNfai 0.813

3 Dividend

DivNw 0.898

DivPat 0.867

EdivEcap 0.84

5 Operating Cash Flow

OcafSale 0.874

OcafInt 0.853

OcafWcap 0.851

6 Debt to EquityDbtEq 0.969

Capgear 0.955

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.*. Rotation converged in 6 iterations.

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CONCLUSION

According to the rotated component matrix (Table 4), looking for highest loading variable

under each factor, the control variables were chosen. The factors named appropriately basedon the control variables, can be formalized as follows:

Factor 1: Retained profit- Retained profit to total assets is the control variable for thisfactor since it has highest loading on the factor. The ratio along with the other ratios-retained

profit as % of- capital expenditure, netwoth, total debt, working capital, and profit after tax,explains about the retained profit position of the firm. Retained profit are is amount of net

profits retained within the company as reserves which can be used at difficult times and notdistributed as dividend. These are available for use within the firm for improvements.

Factor 2: Working capital- Working capital as % total assets, sale and borrowing explain

about the working capital. the ratio under this factor explains whether the firm is capable ofgenerating required working capital. Working capital represents the difference in the currentassets and the current liabilities.

Factor 3: Incremental assets turnover: Value of production to incremental capitalexpenditure ratio is the control variable. The other variables under the factor are from thesame category- VOP to total assets and VOP to net fixed assets. All the 3 ratios explain thefirms incremental assets turnover position. Assets turnover is the efficiency of a company'suse of itsassets in generating sales revenue or sales income to the company, and needs to bemaximised.

Factor 4:Dividend:The higher loading variable is equity dividend as % of networth. Thishelps in knowing the dividend paying capacity, which depends on the earning of thecompany. Shareholders look at profit for investment they make. It is therefore necessary tosee that firms profitability increases.

Factor 5:Operating cash flow- Operating cash flow to sales is the control variable, andalong with operating cash flow to interest, operating cash flow to working capital ratiosexplains the operating cash flow position of the firm. Operating cash flow refers to theamount of cash a company generates and from the revenues it brings in, excluding costsassociated with long-term investment oncapital items or investment insecurities.This rationeeds to be maximised by the firm.

Facotor 6:Financial leverage factor- The ratios in this factor are debt equity ratio andcapital gearing ratio and the factor explains leverage position of the firm. Leverage isconcerned with the use of firms debt finance. Bankers and suppliers of raw materials an ddebenture holders look at the firms debt paying capacity. Therefore, the firms needs to have

strong debt paying capacity.

Hence the firm needs to consider these factors and take steps to improve the position bylooking at the areas which lead to increased cost of the firms resources.

To summarize, it is possible to extract the hidden information available in the database

through this methodology, which aid the managers to govern the productivity enhancement.
http://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Cashhttp://en.wikipedia.org/wiki/Companyhttp://en.wikipedia.org/wiki/Revenuehttp://en.wikipedia.org/wiki/Costhttp://en.wikipedia.org/wiki/Investmenthttp://en.wikipedia.org/wiki/Financial_capitalhttp://en.wikipedia.org/wiki/Securitieshttp://en.wikipedia.org/wiki/Securitieshttp://en.wikipedia.org/wiki/Financial_capitalhttp://en.wikipedia.org/wiki/Investmenthttp://en.wikipedia.org/wiki/Costhttp://en.wikipedia.org/wiki/Revenuehttp://en.wikipedia.org/wiki/Companyhttp://en.wikipedia.org/wiki/Cashhttp://en.wikipedia.org/wiki/Asset

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This methodology will be an integrated tool to surface out controlling parameters, when

integrated with system dynamics modeling.

Appendix-1

Whole set of Financial Ratios collected from CMIE Prowess database and coding used in the study *

No. Classification and description Code

No. Classification and description Code

Profitability ratios 43 Operating cash flow to sales OcafSale

Margins ratios (%) 44 Quick ratio Quick

As % of gross sales Medium to long term liquidity

1PBDIT (NNRT)

PbditnGsale

45Current ratio Current

2PBDT (NNRT)

PbdtnGsale

46Capital gearing ratio Capgear

3PBIT (NNRT)

PbitnGsale

47Debt equity ratio DbtEq

4PBT (NNRT)

PbtnGsale Working capital as % of

5PAT (NNRT)

PatnGsale

48Sales

WcapSal

e

6Operating cash flow

OcafGsale

49Borrowings

WcapBo

r

As % of net sales50

Total assets

WcapTas

s

7 PBDIT (NNRT)PbditnNsale

51Interest incidence (%) IntInc

8PBDT (NNRT)

PbdtnNsale Interest cover

9 PBIT (NNRT)

PbitnNsa

le 52 PBIT/ interest PbitInt

10PBT (NNRT)

PbtnNsale

53PBIT (NNRT) / interest PbitnInt

11PAT (NNRT)

PatnNsal

e54

PBDIT (NNRT) / interest PbditnInt

12Operating cash flow

OcafNsal

e55

Cash profits / interest

CashprI

nt

13Corporate tax as per cent of

PBT CtaxPbt56

Operating cash flow / interest OcafInt

Returns ratios (%) 57 Current assets Cass

As % of total assets 58 Current liabilities Clib

14

PBDIT (NNRT)

PbditnTa

ss

59

Working capital Wcap15

PBDT (NNRT)PbdtnTass

60Net worth Networ

16PBIT (NNRT)

PbitnTass

61Reserves & surplus Resur

17 PAT (NNRT) PatnTass Asset utilisation ratios

As % of GFA Asset turnover ratios

18PBDIT (NNRT)

PbditnGfa

62VOP / total assets VopTass

19PBDT (NNRT)

PbdtnGfa

63VOP / gross fixed assets VopGfa

20 PBIT (NNRT) PbitnGfa 64 VOP / net fixed assets VopNfa

21PAT (NNRT) PatnGfa

65VOP / capital employed

VopCap

eAs % of net worth 66 VOP / current assets VopCass

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22PBIT (NNRT) PbitnNw

Incremental assets turnoverratios

23 PAT (NNRT) PatnNw 67 VOP / total assets VopTassi

As % of capital employed 68 VOP / gross fixed assets VopGfai

24 PBDIT (NNRT)PbditnC

ape69

VOP / net fixed assets VopNfai

25PBDT (NNRT)

PbdtnCape

70VOP / capital employed

VopCapei

26PBIT (NNRT)

PbitnCape

71Incremental capital output ratio Incapout

Appropriation of profits (as %of PAT) Inventory / working capital cycle

27 Dividends DivPat Inventory management (times)

28 Equity dividends EdivPat 72 Raw materials turnover Rmtur

29 Preference dividends PdivPat 73 Finished goods turnover Fgtur

30 Retained profits RpPat # 74 Debtors turnover Debstur

Retained profits as % of 75 Creditors turnover Credstur

31 Gross fixed assets RpGfa 76 Stock accumulation rate (%) Staccr

32 Working capital RpWcap 77Inventories / gross working

capital (%)InvGwca

p

33Capital employed RpCape

78Inventories / net working

capital (%)InvNwca

p

34 Total debt RpTdbt Working capital cycle

35 Total assets RpTass Holding period (no. of days)

36 Net worth RpNw 79 Raw materials & spares RmspHp

37 Dividends / net worth DivNw 80 Production ProdHp

38Equity dividends / equity capital

EdivEca

p81

Finished goods FgHp

39Equity dividends / equity cap. &sh. prem.

EdivEcaps

82 Debtors DebsHp

Liquidity ratios83

Gross working capital cycle

GwcapC

yc

Short term liquidity84

Credit availed from creditorsCredavCred

40Cash to current liabilities CaCl

85Net working capital cycle

NwcapC

yc

41Cash to avg. daily cost of

sales (days)

CaAdcos86

Gross working capital req.GwcapReq

42Operating cash flow to

working capital

OcafWca

p87 Net working capital req.

NwcapR

eq

* The final set of 35 variables used for study are italicized and marked with #.

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