Asset management efficiency of selected cement companies in tamil
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Asset management efficiency of selected cement companies in tamil

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  • 1. INTERNATIONAL JOURNAL OF (2013) – 6502(Print), ISSN (IJM) International Journal of Management (IJM), ISSN MANAGEMENT 0976 – 6510(Online), Volume 4, Issue 1, January- February 0976ISSN 0976-6502 (Print)ISSN 0976-6510 (Online)Volume 4, Issue 1, January- February (2013), pp. 175-182 IJM© IAEME: www.iaeme.com/ijm.aspJournal Impact Factor (2012): 3.5420 (Calculated by GISI) ©IAEMEwww.jifactor.com ASSET MANAGEMENT EFFICIENCY OF SELECTED CEMENT COMPANIES IN TAMIL NADU 1 2 Dr.V.Sarangarajan Dr.S.A.Lourthuraj 1 Director, Christhuraj Institute of Management, CRC, Panjappur,Trichy- 620 012 2 Asst. Professor, Jamal Institute of Management, Jamal Mohammed College, Trichy-20 ABSTRACT This study is focused on cement industry in Tamil Nadu. The aim of the study is to find out the Asset Management efficiency from 1996-1997 to 2005-2006. The authors employed Data Envelopment Analysis by an application of KonSI DEA Analysis for Benchmarking Software Professional Version to find out the Asset efficiency of cement industry in Tamil Nadu. Addition to the Data Envelopment Analysis the authors employed bar chart with the help of Minitab software version15. The conclusion drawn is that the cement industry in Tamil Nadu have efficiently utilized their fixed assets like land, building, plant, furniture, vehicle etc. and current assets like debtors , stock , cash to maximize the return on shareholders’ wealth through increasing sales except during the year 1997- 1998,1998-1999 and 2002-2003. However, looking at asset utilization efficiency the individual company level, assets have been efficiently utilized by Madras Cements and India Cements. Keywords: DEA analysis, Asset Efficiency, Asset Management, Cement Industry, and Financial Performance. I. INTRODUCTION DEA measures efficiency of a Decision Making Unit (DMU) by maximizing the ratio of weighted outputs over weighted inputs. This ratio is normalized according to best practical peers and efficiency is calculated to be between 0 and 1, as 1 representing efficient unit. There are basically two types of DEA models: Charnes et al. (1978) introduced the constant returns to- scale (CRS) and Banker et al. (1984) introduced the variable returns-to-scale (VRS) model. DEA models are also classified as input-oriented, output-oriented or additive (both inputs and outputs are optimized in the best interest of the evaluated unit) based on the direction of the projection of the inefficient unit onto the frontier surface. In this research the authors make use of DEA in cement industry to find out the Asset Management efficiency. 175
  • 2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)II. REVIEW OF PREVIOUS STUDIES The use of financial ratios by financial analysts, lenders, academic researchers, and smallbusiness owners has been widely acknowledged in the literature for more than 40 years. It isacknowledged by the studies of Horrigan (1965), Edmister (1972), Osteryoung & Constand(1992), Devine & Seaton (1995) and Burson (1998). Financial ratios are used to determine acompany’s strengths and weaknesses. A fundamental definition of any profit-seeking business isan entity that acquires resources in order to generate profits through the production and sale ofgoods and/or services. Ratios show important relationships between a firm’s resources and itsfinancial flows. Manandhar and Tang (2002) incorporated intangible aspects, e.g. the internal servicequality, into DEA. They considered internal service quality, operating efficiency and profitabilityas dimensions of performance. Portela and Thanassoulis (2007) analyzed the three dimensions of branch performance:Usage of new transaction channels, efficiency in increasing sales and customer base andgenerating profits. Relations between operational and profit efficiencies and also transactionaland operational efficiencies were identified. Comparison of different dimensions allows us to seesuperior and inferior branches. They found positive links between operational and profitefficiency and also between transactional and operational efficiency. Service quality is positivelyrelated with operational and profit efficiency. Giokas (2008) also studied the efficiency of 44 branches in Greece by searching threeperspectives: Efficiency in managing the economic record of the branches (productionefficiency), efficiency in meeting the demand for transactions with customers (transactionefficiency) and efficiency in generating profits (profit efficiency). All models indicated that thereis a scope for substantial efficiency improvements and again all models identified essentially thesame worst performing branches. Gaganis et al. (2009) examined the profit efficiency, the effect of risk factor (loan lossprovisions) on profit efficiency and the Total Factor Productivity (TFP) change. In the secondstage they analyzed the impact of some internal and external parameters, such as personnel,income per capita, loans to total assets ratio, loans to deposit ratio, return on assets, on efficiency. James Clausen (2009) denotes that about the total asset ratio. The calculation uses twofactors, total revenue and average assets to determine the turnover ratio. When calculating for aparticular year, the total revenue for that year is used. Instead of using the year ending asset totalfrom the balance sheet, a more accurate picture would be to use the total average assets for theyear. Once the average assets are determined for the same time period that revenue is compared,the formula for calculating the asset turnover ratio is. Total Revenue / Average Assets = AssetTurnover Ratio. Paradi et al (2010) evaluated the bank branch efficiency in two stages. From the pointthat a single perspective evaluation cannot fully reflect a branch’s multi-function nature, they firstmeasured production, profitability and intermediation efficiency of branches and then aggregatedthe results with modified Slack Based Model to generate a composite performance index for eachbranch.III. METHODOLOGY The pooled data collection is to assess the impact of regulation on performance of cementcompanies in Tamil Nadu over the time horizon viz., 1996-97 to 2005-06. The approach tomacroeconomic variables is time series. The design of the study is based on the secondarysources of information on financial data. The secondary data is practically, a quantitative methodthat requires standardized information in order to define or describe variables or to study therelationships between the variables. 176
  • 3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) The data was tested for suitability using simple statistical tools such as standarddeviation, standard error of the sample. Due to non- accessibility of sensitive company data,the effect of window dressing could not be ascertained. However , Data was accepted asthese were frequently inspected by SEBI and Institute of Charted Accountants of India .The study, it was felt, will be useful if the random sample drawn from the population of cementindustry in the state of Tamil Nadu. T he present study includes India Cements Limited (ICL), Dalmia Cement (Bharat)Limited (DCL), Madras Cements Limited (MCL) and Chettinadu Cement Corporation Limited(CCCL). Data first analyzed and experimented using non- parametric econometric DataEnvelopment Analysis (DEA) programming approach for Scale efficiency.IV. RESULTS AND DISCUSSION Table (1) and figure (1) reveal the Asset efficiency score of ICL. DEA measuresefficiency of a Decision Making Unit (DMU) by maximizing the ratio of weighted outputs overweighted inputs. This ratio is normalized according to best practical peers and efficiency iscalculated to be between 0 and 1, as 1 representing efficient unit. The efficient years (1996-1997-2000-2001 and 2002-2003-2005-2006) have scores one which states that the ICL efficientlymanaged their total assets in these period. India Cements Limited (ICL) efficiently managed theTotal Assets during the study period except in the year 2001-2002. The value 0.9611 is theinefficient score of the year 2001-2002 means that its output can simultaneously be increased by4.04%. The Data Envelopment Analysis clearly states that the ICL is the most efficient companyin so for as asset utilization is concerned.Table 1. Asset Utilization Efficiency Scores of India Cements Limited (ICL), DalmiaCement (Bharat) Limited (DCL), Madras Cements Limited (MCL) and Chettinadu CementCorporation Limited (CCCL) in Tamil Nadu. Efficiency Scores Year/ ICL DCL MCL CCCL Sample Company Industry 1996 1.0000 1.0000 1.0000 1.0000 1.0000 1997 1.0000 1.0000 1.0000 0.9691 0.8890 1998 1.0000 0.9334 1.0000 1.0000 0.8517 1999 1.0000 0.9578 1.0000 0.9758 1.0000 2000 1.0000 0.9658 1.0000 1.0000 1.0000 2001 0.9611 1.0000 0.9616 1.0000 1.0000 2002 1.0000 0.9043 1.0000 0.8389 0.9128 2003 1.0000 1.0000 1.0000 1.0000 1.0000 2004 1.0000 1.0000 1.0000 1.0000 1.0000 2005 1.0000 1.0000 1.0000 1.0000 1.0000 Inputs: Land, Building, Plant, Furniture, Vehicle, Other Fixed Assets, Stock, Cash and Debtors Output: Sales Model : Output oriented model Scale : Constant returns- to- ScaleSource: Published Annual Reports of the companies, KonSI DEA Analysis for BenchmarkingSoftware Professional Version. 177
  • 4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)Table (1) and figure (2) reveal the Asset efficiency score of DCL. The efficient years (1996-1997,1997-1998, 2001-2002 and 2003-2005) have scores one. The value 0.9043 is the inefficient score ofthe year 2002-2003 means that its output can simultaneously be increased by a factor of 10.58%. Thisis mainly due to capacity addition to the existing facility and also the company has to develop itsCurrent Asset Management. If the assets are efficiently used then the DCL can increase the sales. Figure 1: Asset Utilization Efficiency Scores of India Cements Limited Efficie ncy S cor e of India C e me nts L imite d 1.0 0.8 Efficiency Score 0.6 0.4 0.2 0.0 1996 1997 1998 1999 2000 2001 2002 200 3 200 4 200 5 Year Figure 2: Asset Utilization Efficiency Scores of Dalmia Cement (Bharat) Limited Efficiency Score of Dalmia Cement (Bharat) Limited 1.0 0.8 Efficiency Score 0.6 0.4 0.2 0.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YearTable (1) and figure (3) expose the asset efficiency score for the MCL. The efficient yearshave scores one. The value 0.9616 is the inefficient score of the year 2001 means that itsoutput can simultaneously be increased by 3.99%. The MCL has efficiently employed theirAssets except 2001-2002. The Data Envelopment analysis is clearly states that the MCL isnext to ICL in so for as asset utilization is concerned. 178
  • 5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) Figure 3: Asset Utilization Efficiency Scores of Madras Cements Limited Efficiency Scores of Madras Cements Limited 1.0 0.8 Efficiency Score 0.6 0.4 0.2 0.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 yearTable (1) and figure (4) reveal the asset efficiency of CCCL. The efficient years (1996-1997,1998-1999, 2000-2001, 2001-2002 and 2003-2005) have scores one. The value 0.8389 is theinefficient score of the year 2002 means that its output can simultaneously be increased by afactor of 19.20%. This is mainly due to capacity addition and the company has efficientlyused their currents asset. Hence the CCCL has to concentrate to improve the Fixed AssetManagement strategy efficiently to maximize the return on shareholders’ wealth throughincreasing sales. Figure 4: Asset Utilization Efficiency Scores of Chettinadu Cement Corporation Limited Efficiency Score of Chettinadu Cement Corporation Limited 1.0 0.8 Efficiency Score 0.6 0.4 0.2 0.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year 179
  • 6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)From the Data Envelopment Analysis as shown in the Figure 5, Tables 1, 2 and 3 theconclusion drawn is that the cement industry in Tamil Nadu have efficiently utilized theirfixed asset like land, building, plant, furniture, vehicle etc. and current asset like debtors,stock cash to maximize the return in the form of sales except during the year 1997-1998,1998-1999 and 2002-2003. During 1996-97 and 1997-1998 there have been quiet eyeinvestments in financial assets mainly in plant and machinery which have been underutilized.This is mainly due to capacity addition to the existing facilities. During 2001-2002, Plant andMachinery have been largely underutilized. Receivables Management is also not insatisfactory level. During that year, performance of the cement industry in Tamil Naducould have been efficiently had the trade credits have been brought down by at least 30%.The capacity addition during 1997-1998, 1998-1999 is justified by the improvement in theefficiency of utilization in the subsequent year. The growth in housing and otherinfrastructure sector during 2001-2003, has led to capacity addition during 1997-1998 and1998-1999. The relaxation in bank finance and lower cost of borrowings for housing has ledto spurt in construction industry.Table 2: Virtual inputs/ outputs – Industry. OTHER FURNI Year LAND PLANT FIXED TURE ASSET 1996-97 16,688.88 0.00% 69,944.43 0.00% 778.42 0.00% 32,960.99 0.00% 1997-98 20,439.64 4.04% 85,489.80 24.24% 943.29 12.03% 39,098.16 0.00% 1998-99 30,173.89 7.97% 123,148.57 14.15% 1,215.94 9.69% 35,453.00 0.00% 1999-00 44,558.03 0.00% 177,648.86 0.00% 1,552.51 0.00% 21,704.35 0.00% 2000-01 48,877.87 0.00% 187,198.87 0.00% 1,830.64 0.00% 20,222.01 0.00% 2001-02 51,024.28 0.00% 196,211.25 0.00% 2,301.89 0.00% 13,001.00 0.00% 2002-03 56,554.25 0.00% 229,555.20 2.00% 1,980.33 18.14% 12,187.17 0.00% 2003-04 57,627.34 0.00% 217,699.87 0.00% 2,442.21 0.00% 14,402.34 0.00% 2004-05 75,490.34 0.00% 303,205.56 0.00% 2,609.01 0.00% 10,559.57 0.00% 2005-06 75,039.51 0.00% 306,725.47 0.00% 2,526.53 0.00% 15,777.17 0.00%Source: KonSI DEA Analysis for Benchmarking Software Professional Version. 180
  • 7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)Table 2: (Continued) Virtual inputs/ outputs – Industry. Year STOCK CASH DEBTORS SALES 1996-97 23,389.57 0.00% 8,565.44 0.00% 6,068.75 0.00% 152,462.63 0.00% 1997-98 28,120.92 4.98% 10,215.32 1.03% 7,420.66 0.00% 183,467.90 12.49% 1998-99 32,307.75 8.76% 10,258.63 14.66% 10,744.13 0.00% 213,718.68 17.41% 1999-00 35,037.20 0.00% 8,511.75 0.00% 15,576.08 0.00% 236,965.33 0.00% 2000-01 41,129.44 0.00% 7,674.86 0.00% 23,072.11 0.00% 248,903.03 0.00% 2001-02 45,106.77 0.00% 8,081.76 0.00% 27,171.79 0.00% 239,680.23 0.00% 2002-03 45,967.76 0.05% 6,788.04 6.47% 23,455.35 30.40% 258,300.13 9.55% 2003-04 39,457.66 0.00% 7,301.09 0.00% 22,447.19 0.00% 255,174.92 0.00% 2004-05 41,082.43 0.00% 9,794.21 0.00% 22,903.79 0.00% 267,541.09 0.00% 2005-06 60,279.77 0.00% 8,622.72 0.00% 29,942.78 0.00% 341,425.59 0.00%Source: KonSI DEA Analysis for Benchmarking Software Professional Version.Fig 5: Asset Utilization Efficiency for the Sample Total of Tamil Nadu Cement Industry Efficiency Score for the Sample Total of Tamil Nadu Cement Industry 1.0 0.8 Efficiency Score 0.6 0.4 0.2 0.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year 181
  • 8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)V CONCLUSION The conclusion drawn is that the cement industry in Tamil Nadu have efficientlyutilized their fixed assets like land, building, plant, furniture, vehicle etc. and current assetslike debtors , stock cash to maximize the return in the form of sales except during the year1997-1998,1998-1999 and 2002-2003. However, looking at asset utilization efficiency the individual company level, assetshave been efficiently utilized by Madras Cements and India Cements. Finally, the assetutilization to generate volume in terms of sales by cement industry in Tamil Nadu will besatisfactory if the assets are efficiently used.REFERENCES 1) Banker RD, Charnes A, Cooper WW (1984). Some Models for Estimating Technical and Scale Inefficiency in Data Envelopment Analysis. Manage. Sci., 30(9):1078- 1092. 2) Burson, R. (1998). Tools you can use for improved ratio analysis, San Diego Business Journal, Vol. 19, Issue 49, pp. 19-23. 3) Charnes A, Cooper WW, Rhodes E (1978). Measuring the Efficiency of Decision Making Units. Eur. J. Oper. Res., l: 2(6):429-444. 4) Causen James. (2009). “Basic Accounting 101- Asset Turnover Ratio: Inventory, Cash, Equipment and Accounts Receivable Analysis”, Journal of asset turnover ratio 5) Devine, K. and Seaton, L (1995). An examination of quarterly financial ratio stability: Implications for financial decision making, Journal of Applied Business Research, winter, pp. 81-98. 6) Edmister, R. O. (1972). Financial ratios as discriminant predictors of small business failure, Journal of Finance, Vol. 27, No. 1, pp. 139-140. 7) Gaganis C, Liadaki A, Doumpos M, Zopounidis C (2009). Estimating and analyzing the efficiency and productivity of bank branches: Evidence from Greece. Manag. Financ. 5(2):202-218 8) Giokas DI (2008). Assessing the Efficiency in Operations of a Large Greek Bank Branch Network Adopting Different Economic Behaviors. Econ. Model. 25(3):559– 574. 9) Horrigan, J. O. (1965) some empirical bases of financial ratio analysis, The Accounting Review, Vol.40, No. 3, pp. 558-568. 10) Manandhar R, Tang JCS (2002). The evaluation of bank branch performance using Data envelopment analysis a framework. J. High Technol. Manage. Res., 13(1):1–17. 11) Osteryoung, J. and Constand, R. (1992). Financial ratios in large public and small private firms, Journal of Small Business Management, pp. 35-47 12) Portela MCAS, Thanassoulis, E (2007). Comparative Efficiency Analysis of Portuguese Bank Branches. Eur. J. Oper. Res., 177(2): 275 -1288. 13) Paradi JC, Rouattb S, Zhu H (2010). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega. 39(1): 99-109. 182