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
0976
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 4, Issue 1, January- February (2013), pp. 175-182
IJM
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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.
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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 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 (1998). 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 (2002) 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., 1996-97 to 2005-06. 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.
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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 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.
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 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 (1996-1997-
2000-2001 and 2002-2003-2005-2006) 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 2001-2002. The value 0.9611 is the
inefficient score of the year 2001-2002 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 Efficiency Scores of India Cements Limited (ICL), Dalmia
Cement (Bharat) Limited (DCL), Madras Cements Limited (MCL) and Chettinadu Cement
Corporation 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- Scale
Source: Published Annual Reports of the companies, KonSI DEA Analysis for Benchmarking
Software Professional Version.
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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 of
the year 2002-2003 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 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
Year
Table (1) and figure (3) expose the asset efficiency score for the MCL. The efficient years
have scores one. The value 0.9616 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 2001-2002. The Data Envelopment analysis is clearly states that the MCL is
next to ICL in so for as asset utilization is concerned.
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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
year
Table (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 the
inefficient score of the year 2002 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 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
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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 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 1997-1998,
1998-1999 and 2002-2003. During 1996-97 and 1997-1998 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 2001-2002, 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 1997-1998, 1998-1999 is justified by the improvement in the
efficiency of utilization in the subsequent year. The growth in housing and other
infrastructure sector during 2001-2003, has led to capacity addition during 1997-1998 and
1998-1999. 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.
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.
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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
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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 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
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. 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):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.
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