International Journal of Management (IJM), ISSN (Print), ISSN (Online), Volume 4, Issue 1, January- February (2013)

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1 INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) ISSN (Print) ISSN (Online) Volume 4, Issue 1, January- February (2013), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJM I A E M E ASSET MANAGEMENT EFFICIENCY OF SELECTED CEMENT COMPANIES IN TAMIL NADU Dr.V.Sarangarajan 1 Dr.S.A.Lourthuraj 2 1 Director, Christhuraj Institute of Management, CRC, Panjappur,Trichy 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 - to 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 -,- and -. 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 II. REVIEW OF PREVIOUS STUDIES The use of financial ratios by financial analysts, lenders, academic researchers, and small business owners has been widely acknowledged in the literature for more than 40 years. It is acknowledged by the studies of Horrigan (1965), Edmister (1972), Osteryoung & Constand (1992), Devine & Seaton (1995) and Burson (). Financial ratios are used to determine a company s strengths and weaknesses. A fundamental definition of any profit-seeking business is an entity that acquires resources in order to generate profits through the production and sale of goods and/or services. Ratios show important relationships between a firm s resources and its financial flows. Manandhar and Tang () incorporated intangible aspects, e.g. the internal service quality, into DEA. They considered internal service quality, operating efficiency and profitability as 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 and generating profits. Relations between operational and profit efficiencies and also transactional and operational efficiencies were identified. Comparison of different dimensions allows us to see superior and inferior branches. They found positive links between operational and profit efficiency and also between transactional and operational efficiency. Service quality is positively related with operational and profit efficiency. Giokas (2008) also studied the efficiency of 44 branches in Greece by searching three perspectives: Efficiency in managing the economic record of the branches (production efficiency), efficiency in meeting the demand for transactions with customers (transaction efficiency) and efficiency in generating profits (profit efficiency). All models indicated that there is a scope for substantial efficiency improvements and again all models identified essentially the same worst performing branches. Gaganis et al. (2009) examined the profit efficiency, the effect of risk factor (loan loss provisions) on profit efficiency and the Total Factor Productivity (TFP) change. In the second stage 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 two factors, total revenue and average assets to determine the turnover ratio. When calculating for a particular year, the total revenue for that year is used. Instead of using the year ending asset total from the balance sheet, a more accurate picture would be to use the total average assets for the year. 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 = Asset Turnover Ratio. Paradi et al (2010) evaluated the bank branch efficiency in two stages. From the point that a single perspective evaluation cannot fully reflect a branch s multi-function nature, they first measured production, profitability and intermediation efficiency of branches and then aggregated the results with modified Slack Based Model to generate a composite performance index for each branch. III. METHODOLOGY The pooled data collection is to assess the impact of regulation on performance of cement companies in Tamil Nadu over the time horizon viz., -97 to -06. The approach to macroeconomic variables is time series. The design of the study is based on the secondary sources of information on financial data. The secondary data is practically, a quantitative method that requires standardized information in order to define or describe variables or to study the relationships between the variables. 176

3 The data was tested for suitability using simple statistical tools such as standard deviation, 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 as these 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 cement industry in the state of Tamil Nadu. The 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 Data Envelopment Analysis (DEA) programming approach for Scale efficiency. IV. RESULTS AND DISCUSSION Table (1) and figure (1) reveal the Asset efficiency score of ICL. 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. The efficient years ( and ) have scores one which states that the ICL efficiently managed their total assets in these period. India Cements Limited (ICL) efficiently managed the Total Assets during the study period except in the year The value is the inefficient score of the year means that its output can simultaneously be increased by 4.04%. The Data Envelopment Analysis clearly states that the ICL is the most efficient company in so for as asset utilization is concerned. Table 1. Asset Utilization s of India Cements Limited (ICL), Dalmia Cement (Bharat) Limited (DCL), Madras Cements Limited (MCL) and Chettinadu Cement Corporation Limited (CCCL) in Tamil Nadu. Year/ Company s ICL DCL MCL CCCL Sample Industry Inputs: Land, Building, Plant, Furniture, Vehicle, Other Fixed Assets, Stock, Cash and Debtors Output: Sales Model : Output oriented model Scale : Constant returns- to- Scale Source: Published Annual Reports of the companies, KonSI DEA Analysis for Benchmarking Software Professional Version. 177

4 Table (1) and figure (2) reveal the Asset efficiency score of DCL. The efficient years (-, -, and -) have scores one. The value is the inefficient score of the year - means that its output can simultaneously be increased by a factor of 10.58%. This is mainly due to capacity addition to the existing facility and also the company has to develop its Current Asset Management. If the assets are efficiently used then the DCL can increase the sales. Figure 1: Asset Utilization s of India Cements Limited Efficiency S core of India Cements L imited Year Figure 2: Asset Utilization s of Dalmia Cement (Bharat) Limited of Dalmia Cement (Bharat) Limited Year Table (1) and figure (3) expose the asset efficiency score for the MCL. The efficient years have scores one. The value is the inefficient score of the year 2001 means that its output can simultaneously be increased by 3.99%. The MCL has efficiently employed their Assets except The Data Envelopment analysis is clearly states that the MCL is next to ICL in so for as asset utilization is concerned. 178

5 Figure 3: Asset Utilization s of Madras Cements Limited s of Madras Cements Limited year Table (1) and figure (4) reveal the asset efficiency of CCCL. The efficient years (-, -, , and -) have scores one. The value 389 is the inefficient score of the year means that its output can simultaneously be increased by a factor of 19.20%. This is mainly due to capacity addition and the company has efficiently used their currents asset. Hence the CCCL has to concentrate to improve the Fixed Asset Management strategy efficiently to maximize the return on shareholders wealth through increasing sales. Figure 4: Asset Utilization s of Chettinadu Cement Corporation Limited of Chettinadu Cement Corporation Limited Year 179

6 From the Data Envelopment Analysis as shown in the Figure 5, Tables 1, 2 and 3 the conclusion drawn is that the cement industry in Tamil Nadu have efficiently utilized their fixed 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 -, - and -. During -97 and - there have been quiet eye investments 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-, Plant and Machinery have been largely underutilized. Receivables Management is also not in satisfactory level. During that year, performance of the cement industry in Tamil Nadu could have been efficiently had the trade credits have been brought down by at least 30%. The capacity addition during -, - is justified by the improvement in the efficiency of utilization in the subsequent year. The growth in housing and other infrastructure sector during 2001-, has led to capacity addition during - and -. The relaxation in bank finance and lower cost of borrowings for housing has led to spurt in construction industry. Table 2: Virtual inputs/ outputs Industry. Year LAND PLANT FURNI TURE OTHER FIXED ASSET , % 69, % % 32, % 20, % 85, % % 39, % 30, % 123, % 1, % 35, % 44, % 177, % 1, % 21, % 48, % 187, % 1,834 0% 20, % 51, % 196, % 2, % 13,000 0% 56, % 229, % 1, % 12, % 57, % 217, % 2, % 14, % 75, % 303, % 2, % 10, % 75, % 306, % 2, % 15, % Source: KonSI DEA Analysis for Benchmarking Software Professional Version. 180

7 Table 2: (Continued) Virtual inputs/ outputs Industry. Year STOCK CASH DEBTORS SALES , % 8, % 6, % 152, % 28, % 10, % 7,426 0% 183, % 32, % 10, % 10, % 213, % 35, % 8, % 15, % 236, % 41, % 7, % 23, % 248, % 45, % 8, % 27, % 239,683 0% 45, % 6, % 23, % 258, % 39, % 7,309 0% 22, % 255, % 41, % 9, % 22, % 267,549 0% 60, % 8, % 29, % 341, % Source: KonSI DEA Analysis for Benchmarking Software Professional Version. Fig 5: Asset Utilization Efficiency for the Sample Total of Tamil Nadu Cement Industry for the Sample Total of Tamil Nadu Cement Industry Year 181

8 V CONCLUSION 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 in the form of sales except during the year -,- and -. However, looking at asset utilization efficiency the individual company level, assets have been efficiently utilized by Madras Cements and India Cements. Finally, the asset utilization to generate volume in terms of sales by cement industry in Tamil Nadu will be satisfactory 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): ) Burson, R. (). Tools you can use for improved ratio analysis, San Diego Business Journal, Vol. 19, Issue 49, pp ) Charnes A, Cooper WW, Rhodes E (1978). Measuring the Efficiency of Decision Making Units. Eur. J. Oper. Res., l: 2(6): ) 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 ) Edmister, R. O. (1972). Financial ratios as discriminant predictors of small business failure, Journal of Finance, Vol. 27, No. 1, pp ) 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): ) Giokas DI (2008). Assessing the Efficiency in Operations of a Large Greek Bank Branch Network Adopting Different Economic Behaviors. Econ. Model. 25(3): ) Horrigan, J. O. (1965) some empirical bases of financial ratio analysis, The Accounting Review, Vol.40, No. 3, pp ) Manandhar R, Tang JCS (). The evaluation of bank branch performance using Data envelopment analysis a framework. J. High Technol. Manage. Res., 13(1): ) Osteryoung, J. and Constand, R. (1992). Financial ratios in large public and small private firms, Journal of Small Business Management, pp ) Portela MCAS, Thanassoulis, E (2007). Comparative Efficiency Analysis of Portuguese Bank Branches. Eur. J. Oper. Res., 177(2): ) Paradi JC, Rouattb S, Zhu H (2010). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega. 39(1):