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Transcript of Efficiency of Steel Comp
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7/28/2019 Efficiency of Steel Comp
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1
Efficiency Measurement of Indian Steel Industry using Data
Envelopment Analysis (DEA)
Dr. Amit Kumar DwivediAsst. Faculty
Entrepreneurship Development Institute of India (EDII)P.O.: Bhat-382428, Gandhinagar, Gujarat (India)
E-mail:[email protected]
&
Priyanko GhoshResearch Assistant
Indian Institute of Management-AhmedabadVastrapur, Gujarat (India)
E-mail: [email protected]
Abstract
Data Envelopment analysis (DEA) has been used to calculate the
technical and scale efficiency measures of the public and private steel firms of
the Indian Steel Industry (2006 to 2010). Within DEA framework, the input &
Output oriented Variable Returns to Scale (VRS) & Constant Return to Scale
(CRS) model is employed for the study of Decision making units (DMUs). A
representative sample of 17 public & private firms which account for major
portion of the total market share is studied. The selection criterion for the
inclusion of a firm in the analysis has been total sales of INR 500 crores or more
in the year 2010. No study has been done in the context of Indian steel industry
in the Post-liberalization era which motivates us to initiate the study. It was found
from the result that the Tata Steels Limited has showed high efficiency over aperiod time than remaining steel producing firms in India.
Keywords: Technical Efficiency, Indian Steel Industry, DEA, Input /Outputoriented.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected] -
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2
(I)Introduction
Worlds fifth largest crude steel producers India1is expected to become
the second largest producer after China by 2015-16. The Indian steel industry in
the last two decades of the controlled regime was plagued by low growth rates. A
need was felt to break the vicious circle of low growth rate, shortages and
structural inefficiencies. As a part of the general economic reforms programme,
deregulation of the Indian steel industry was initiated in 1992. The new policy
regime consisted of measures such as decontrol of price and distribution, de-
licensing / de-reservation of capacity, progressive reduction of tariff barriers and
removal of quantitative restrictions in international trade. The National Steel
Policy 2005 had projected consumption to grow at 7 to 8 per cent based on a
GDP growth rate of 7-7.5% and production of 110 million tone by 2019-20. These
estimates will be largely exceeded and it has been assessed that, on a 'most
likely scenario' basis, the crude steel production capacity in the country by the
year 2011-12 will be nearly 124 million tone. The steel demand started gathering
speed post April 2009 and steel consumption grew by 7 to 8 per cent in the first
nine months of the fiscal ending March 2010. Indias top steelmakers posted
double-digit growth in sales, backed by robust demand from automobile,construction and infrastructure sectors (Sachdeva, 2010). Indian per capita steel
consumption is only around 47 Kg. (2008) against the world averages of 190 Kg.
and that of 400 Kg. in developed economies.
Table 1: World Crude Steel Production in 2009*
Rank Country Production (Million Tones)
1 China 567.8
2 Japan 87.5
3 Russia 59.9
4 USA 58.1
1Based on rankings released by World Steel Association.
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5 India 56.6
6 South Korea 48.6
7 Germany 32.7
8 Ukraine 29.8
9 Brazil 26.5
10 Turkey 25.3
Source: World Steel Association; * Provisional.
By tradition, Indian steel industry has been classified into public and
private Producers. The latter comprises of various steel making plants producing
crude steel/finished steel (long product/flat product)/ pig iron/ sponge iron and are
spread across the different states of the country.
(II)
Literature Survey
Studying the exhaustive literature it was found that no study has been
done in the era of post-liberalisation on Indian Steel industry. A few studies on
iron and steel industry of China have used variety of specifications for inputs and
outputs as discussed in the following table. The efficiency scores are relativelysensitive to the measurement in terms of inputs and outputs.
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Ta
ble2:Review
ofLiterature
onEfficiencyMeasurementonDifferentIndustries
Sl.
Yea
Authors
Methodology
BriefRecommendatio
ns
Scope
AssessmentParameters/
Drivers
01
1995
Subhash
C.
Ray
&
HiungJoonKim
Non
Parametric
Analysis
using
Data
EnvelopmentAnalysis
Considerable
reduction
inth
e
cost
of
production
could
have
beena
chieved
by
eliminating
technical
and
Allocative
inefficiencies
without
introduc
ing
further
technologicalimprovementsbey
ondwhatis
evident.
Costefficiencyin
the
US
steel
industry:
A
nonparametric
analysisusingdata
envelopment
analysis
Single
composite
output
and
three
inputs
-labor,
capital,andmaterials-are
Considered.
02
2002
JinlongMa,
Da
vidG.
Evans,
Robe
rt
J.
Fuller,
Donald
F.
Stewart
Data
Envelopment
Analysis
(DEA)
approach
and
MalmquistProductivity
index
were
used
to
measure
technical
efficiency
and
the
changesinproductivity.
Product
structure
showed
the
strongest
correlation
with
technicaleffic
iency,with
enterprises
producing
onlyfin
ished
steel
productshavingbyfarthehighesttechnical
efficiencyandthoseproducingonlypigiron
byfarthelowest.
Technicalefficiency
and
productivity
change
ofChinas
iron
and
steel
industry
Studyconsideredenergyas
a
separate
inputfactorin
theanalysesandotherinput
variables
include
labour,
fixed
capitaland
working
Capital.
Value
of
Products
as
Output.
03
1995
YanruiWu
A
stochastic
frontier
analysis
Studyshowssignificanteffectsonefficiency
offirm
ownership
and
theec
onomies
of
scale.
The
productive
efficiency
of
Chinese
iron
and
steel
firms:
A
stochastic
frontier
analysis.
Net
Value
of
Output,
Employment,NetValueof
Assets,
CapitalLabor-Ratio,
Age,
CrudeSteel,PigIron,
SteelProducts.
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5
04
1991
RaghbendraJha,
M.N.
Murty,
SatyaPauland
BalbirS.
Sahni
Leontiefaverage
cost
function
Labourandcapitalaregoodsub
stitutesand
soarelabourandenergymate
rial.Capital
andenergymaterialarecomplementaryin
theproductionof
ironandsteel
Cost
structureof
Indias
iron
and
steel
industry:
Allocative
efficiency,
economiesofscale
and
biased
technicalprogress
labour,capital,energy
andmaterialinputs.
05
1986
MoneerAalam
Farrells
index
of
technicalefficiency
severalcases
the
smallerestablishments
turnouttobetechnically
more
efficientas
comparedtotheircounterparts
inmediumandlargesizes
Technical
Efficiencyin
Indian
Manufacturing
Industries:
An
Analysis
by
EstablishmentSize
Capital/outputratioand
Labour/outputratio.
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(III)
Research Methodology
For last few decades firms are interested to evaluate their performances
over their competitors in terms of efficiency. According to Farrell (1957)
efficiency can be decomposed into two parts, Technical Efficiency (TE) and
Allocative Efficiency (AE). TE considers attaining the maximal output of a
Decision Making Units (DMU) given a set of inputs whereas AE considers
optimal allocations of inputs given the set of prices of the products. Total
Economic Efficiency can be computed from these two efficiency measures.
Efficiency can be viewed from input and output orientation.
Suppose a firm operates on two inputs (X1 and X2) to produce a single
output Y. So the production function can be given as below
Y = f (X1, X2)
This equation can be rewritten as follows
1 = f (X1/Y, X2/Y) (Assuming constant returns to scale)
In input oriented measure the basic principle is that we reduce inputs
without changing the amount of output. In the following figure LL is the efficient
unit isoquant with a given level of input level OU.
L
X1/Y
X2/YL
V
U
X
Q
QO
W
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Suppose a firm operates at U level of input and W is an efficient point as it
lies on the efficient unit isoquant. UW level of input can be reduced without
reducing the amount of output. This amount is the measurement of inefficiency.
The amount of efficiency must be one minus the level of inefficiency. So from the
diagram Technical Efficiency can be measured by the ratio of OW/OU which is
one minus the level of inefficiency. If input prices are known that is shown by the
line QQ a firm can reduce its production cost by the amount of WV such that it
can operate on X which is efficient both technically and allocatively rather than W
which is only technically efficient. So Allocative Efficiency is given by the ratio
OV/OW.
Total Economic Efficiency can be given by E = OV/OU = OV/OW *OW/OU = Technical Efficiency * Allocative Efficiency
As all efficiency measures are ratio they range between zero and one. In output
oriented measure we evaluate the expansion of output without changing the level
of inputs. We assume firm produces two outputs (Y1 and Y2) using one input (X).
In the following figure BB is the production possibility curve where each and
every firm is technically efficient.
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Suppose a firm operates at point S which is an inefficient condition as it
lies below the production frontier. So SP is the level of technical inefficiency and
efficiency can be derived by one minus level of inefficiency. So Technical
Efficiency is given by the ratio OS/OP. If we incorporate price information which
is represented by the isoprofit curve AA Allocative Efficiency is given by OP/OR.
Total Economic Efficiency is given by E = OS/OR = OS/OP * OP/OR
=Technical Efficiency * Allocative Efficiency.
The input and output oriented measures of efficiency are same under the
assumption of constant returns to scale and differ when increasing and
decreasing returns to scale exist (Fare and Lovell, 1978).
Farrells (1957) frontier function technique is limited in the sense of
constant returns to scale and non parametric nature. Later these assumptionsare relaxed. Efficiency estimation technique can be divided into two categories.
(1) Econometric techniques
(2) Mathematical programming techniques
(i). Econometric Techniques:
Y1/X
Y2/X
A
B
R
Q
B
O
A
P
S
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These methods involve estimation of production function (primal) or cost
or profit function (dual) to derive the frontier. There are two types of frontiers,
deterministic and stochastic. Ordinary Least Square technique is used to
estimate the deterministic frontier. The major drawback of this method is that it
does not capture the possible effects of the uncontrollable factors of the producer
which results an overestimation of efficiency (Meeusen and van den Broeck,
1977).
Stochastic frontier model carefully handles this problem. Maximum
likelihood methods estimate stochastic frontier model which comprises an error
term that incorporates the possible effects of uncontrollable factors of the
producer. But this methodology needs specific functional form to estimate
efficiency and is limited with respect to the distributional assumptions of the error
term.
(ii). Mathematical Programming Techniques:
Farrells non parametric piecewise convex isoquant is recognized as
mathematical programming technique. His work was strengthened by Charnes,
Cooper and Rhodes (1978), Fare, Grosskopf and Lovell (1983), Banker, Charnes
and Cooper (1984), and Byrens, Fare and Grosskopf (1984). This approach is
widely known as Data Envelopment Analysis (DEA). The major advantage of
DEA is that it does not demand any specification about the functional form or
does not assume any distributional form of the error term. DEA works smoothly
under the assumption of VRS.
A. Analytical Model:
Data Envelopment Analysis (DEA) is a non parametric mathematical
programming to estimate the frontier function. DEA provides the efficiency of
different firms operating on same input output variable. We illustrate DEA method
from both input and output orientation. Let us considerP number of DMU
producing Q number of outputs using R number of inputs. Inputs are denoted
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as ipx ( i =1,2, R ) and outputs are denoted as jpy ( j =1,2, Q ) for each
farm p ( =1,2, P).
We would like to find out the efficiency for each farm and hence its better to get a
ratio of all outputs over all inputs. So we are interested to find out the ratio of
ipi
jpj
xv
yu, where jpy is the quantity of j
thoutput produced by
thfarm, ipx is the
quantity ofi th input used by th farm, ju and iv are the output and input weights
respectively.
So efficiency can be represented as PTE =
=
=
R
i
ipi
Q
j
jpj
xv
yu
1
1 (Coelli,1998; Worthington,
1999).
DMU are interested to maximize their efficiency where efficiency must be less
than one which plays the role of constraint.
The optimization problem becomes
Max PTE
subject to
=
=
R
i
ipi
Q
j
jpj
xv
yu
1
1 1.
where ju and iv 0.
The constraint restricts the efficiency less than one and confirms that weights are
positive. The weights are chosen in such a way that efficiency will be maximized.
From an output oriented viewpoint the mathematical programming can be
formulated as below(Coelli, 1998; Worthington, 1999; Shiu, 2002)
Max PTE
subject to =
Q
j
jpjyu
1
- ipx +w 0 =1,2, P
ipixv -
=
R
i
ipixu
1
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ju and iv 0.
From input orientation method the mathematical programming can be formulated
as follows (Banker and Thrall, 1992; Coelli, 1998; Worthington, 1999; Shiu, 2002;
Topuz et al, 2005).
Min PTE
subject to =
Q
j
jpjyu
1
- jpy + w 0 p=1,2, P
ipx -
=
R
i
ipixu1
0
and ju and iv 0.
Ifw = 0 then the above model follows CRS and if w is unconstrained then it
follows VRS. We get technical efficiency in the first case and pure technical
efficiency in the second case.
B. Selection of Inputs and Outputs:
DEA approach can be applied to revenue producing DMUs. This can be done
by converting the financial performance measures to the DMUs technical
efficiency equivalents. While using input and output variables, we have followed
the methodology of Feroz et. al. (2003) and Wang (2006), who have converted
the financial performance measures to the firms technical efficiency equivalent
using DuPont Model 2 . This process of measuring financial performance
indicators can be converted into output and input variables. Where, sales
revenue and Profit after Tax (PAT) can be used as output variable while cost of
goods sold (COGS), selling and Administration expenses, and total assets as
input variables. The indicators are defined as follows:
1. Input (X1): Total Cost of Goods Sold (COGS)
2The DuPont model is a technique for analyzing a firms profitability using traditional
performance management tools. For enabling this, DuPont model integrates incomestatement elements with balance sheet.
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2. Input(X2): Total Selling and Administration Expenses (or Cost)
3. Input (X3): Total Assets hold by firm during the year
4. Output (Y1): Total Sales of the Firm during the Year
5. Output (Y2): Total Profit after Tax (PAT) of the Firm during the Financial
Year.
The above methodology helps us to logically convert performance ratios
into efficiency. In this way long term resources total assets and short term
resources cost of goods sold and selling and Administration expenses are used
to produce output in the form of sales revenue and PAT.
C. Selection of Data:
A representative sample of 17 public & private firms which account for major
portion of the total market share is studied considering the imitates of DEA only
those firms are included in analysis which have their equity in positive and their
annual reports were available for all the five years from 2006 to 2010. The
selection criterion for the inclusion of a firm in the analysis has been total sales of
INR 800 crores or more. Data for the study is obtained from secondary sources
(www.capitaline.com) in the form of annual reports of the steel firms for the
period 2006 to 2010.
(IV)
Results and discussions
We calculate the efficiency using DEA approach for both constant and
variable returns to scale. We consider both input and output oriented measures
and present the analysis in the following table. We take 17 steel firms of India
and measure the efficiency for a five year period.
Table 3: Two Outputs-Three Inputs DEA Efficiency of Indian Steel Industry
(2006-2010)
Sl.No.
DMU Input Oriented Output Oriented
CRS(TE)
VRS(TE)
CRS(TE)
VRS(TE)
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1 Bhushan Steel 0.79 0.7996 0.79 0.80082 ISMT Ltd 0.8442 0.8442 0.8442 0.87483 Ispat Industries Ltd 0.7808 0.8236 0.7808 0.82584 Jindal Saw Ltd 0.8746 0.8806 0.8746 0.88245 JSW Steel Ltd 0.938 0.9836 0.938 0.9838
6 Lloyds Steel Industries Ltd 0.7936 0.8278 0.7936 0.83967 Mahindra Ugine Steel Firm Ltd 0.9614 0.9776 0.9614 0.98788 Mukand Ltd 0.7666 0.7736 0.7666 0.77889 National Steel & Agro Industries Ltd 0.9678 0.987 0.9678 0.9902
10 Ramsarup Industries Ltd 0.8818 0.9642 0.8818 0.96711 Steel Authority of India Ltd 0.8812 0.9868 0.8812 0.98812 Shah Alloys Ltd 0.7416 0.7466 0.7416 0.800413 Sunflag Iron & Steel Firm Ltd 0.9176 0.9688 0.9176 0.977614 Surya Roshni Ltd 0.956 0.9568 0.956 0.96115 Tata Steel Ltd 0.978 1 0.978 116 Usha Martin Ltd 0.7838 0.7838 0.7838 0.785
17 Uttam Galva Steels Ltd 0.8366 0.855 0.8366 0.8574Mean 0.8643 0.8917 0.8643 0.9000
We compute the efficiency of the firms using CRS and VRS from both
input and output orientation. From input oriented point of view industry efficiency
averages for CRS and VRS are 0.8643 and 0.8917 respectively. Among 17 firms
9 and 8 firms have efficiency more than the industry average for CRS and VRS
respectively. As per output orientation industry efficiency averages are 0.8643
and .9 for CRS and VRS respectively. Again 9 and 8 firms perform better than
the industry efficiency mean for CRS and VRS respectively. Same results from
input and output orientation confirm that our input output combination is well fitted
for the industry.
We provide firm wise five years efficiency for CRS and VRS from input
and output orientation in Annexure 1. We got the same results from both
approaches. We list the firms who achieved efficiency one with their
corresponding years. As per CRS efficient farms are JSW Steel Ltd (2007),Mahindra Ugine Steel Company Ltd (2006, 2009, 2010), National (2009),
Ramsarup Industries Ltd (2006) and Tata Steel (2006, 2008). According to VRS
efficient farms are Jindal(2008), JSW Steel Ltd (2007, 2008, 2010), Mahindra
Ugine Steel Company Ltd (2006, 2009, 2010), National Steel & Agro Industries
Ltd (2006, 2009, 2010), Ramsarup Industries Ltd (2006,2009, 2010), Steel
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Authority of India Ltd (2007, 2008, 2009, 2010), Sunflag Iron & Steel Company
Ltd (2010), Tata Steel (2006,2007,2008,2009,2010).
So it is quite evident that Tata Steel performs better than other DMU
followed by Steel Authority of India for last five years.
We conduct DEA analysis for sugar firms in Indian context. We compute
the efficiency for 43 firms from input and output orientation for last five year
period.
Table 4: Two Outputs-Three Inputs DEA Efficiency of Indian Sugar Industry
(2006-2010)
SL NO DMUSInput Oriented Output Oriented
CRS VRS CRS VRS
1 Bajaj Hindusthan 0.5288 0.923 0.5288 0.99322 Balrampur Chini 0.096 0.3888 0.096 0.7902
3 Dalmia Bharat 0.085 0.464 0.085 0.7792
4 Dhampur Sugar 0.0948 0.1018 0.0948 0.4576
5 EID Parry 0.128 0.2906 0.128 0.609
6 Sakthi Sugars 0.0844 0.0982 0.0844 0.516
7 Sh.Renuka Sugar 0.1358 0.4584 0.1358 0.6688
8 Triven.Engg.Ind. 0.112 0.4714 0.112 0.805
9 Simbhaoli Sugars Ltd 0.088 0.088 0.088 0.313
10 Bannari Amm.Sugar 0.1074 0.1076 0.1074 0.4786
11 DCM Shriram Inds 0.1478 0.1478 0.1478 0.4564
12 Dharani Sugars 0.1484 0.1484 0.1484 0.331213 Jeypore Sug.Co 0.1368 0.1368 0.1368 0.3296
14 JK Sugar 0.3246 0.3246 0.3246 0.3554
15 Kesar Enterprise 0.1954 0.1954 0.1954 0.3222
16 Kothari Sugars 0.1658 0.1658 0.1658 0.3482
17 Parrys Sugar 0.1952 0.1952 0.1952 0.357
18 Ponni Sug.Erode 0.395 0.395 0.395 0.441
19 Rajshree Sugars 0.1124 0.1124 0.1124 0.3446
20 Thiru Aroor. Su. 0.115 0.115 0.115 0.3108
21 Ugar Sugar Works 0.0994 0.0994 0.0994 0.2936
22 Dwarikesh Sugar Industries Ltd 0.1302 0.1302 0.1302 0.3182
23 Eastern Sugar & Industries Ltd 1 1 1 124 Empee Sugars & Chemicals Ltd 0.4434 0.4434 0.4434 0.4704
25 Gayatri Sugars Ltd 0.4036 0.4036 0.4036 0.4252
26 Gobind Sugar Mills Ltd 0.2662 0.2662 0.2662 0.3576
27 Indian Sucrose Ltd 0.2836 0.2836 0.2836 0.3626
28 Kashipur Sugar Mills Ltd 0.7674 0.7686 0.7674 0.7744
29 KCP Sugar & Industries 0.171 0.171 0.171 0.39
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Corporation Ltd
30 KM Sugar Mills Ltd 0.2936 0.2936 0.2936 0.365
31 Naraingarh Sugar Mills Ltd 0.6072 0.6072 0.6072 0.6224
32 Oswal Overseas Ltd 0.6764 0.6764 0.6764 0.6786
33 Oudh Sugar Mills Ltd 0.0864 0.0864 0.0864 0.3184
34 Piccadily Agro Industries Ltd 0.312 0.312 0.312 0.3835 Prudential Sugar Corporation Ltd 0.4862 0.4862 0.4862 0.4956
36 Rana Sugars Ltd 0.1526 0.1526 0.1526 0.3022
37 SBEC Sugar Ltd 0.2358 0.2358 0.2358 0.3356
38 Sri Chamundeswari Sugars Ltd 0.1854 0.1854 0.1854 0.3302
39 United Provinces Sugar Co Ltd 0.2674 0.2674 0.2674 0.3478
40Upper Ganges Sugar &Industries Ltd 0.0896 0.0896 0.0896 0.2762
41 Uttam Sugar Mills Ltd 0.121 0.121 0.121 0.2986
42 Vishnu Sugar Mills Ltd 0.5266 0.5266 0.5266 0.5464
43 Piccadily Sugar & Allied Inds Ltd 0.859 0.859 0.859 0.8616
Mean 0.275828 0.320777 0.275828 0.478084
From input oriented point of view industry average efficiency is 0.2758 and
0.3207 for CRS and VRS respectively. Among 43 firms 15 and 16 firms have
efficiency more than the industry average for CRS and VRS respectively from
input orientation. From output oriented view 15 firms perform better than the
industry average efficiency for both CRS and VRS. Average industry efficiency
for CRS is same either from both measures.
In Annexure 2 we provide year wise efficiency of 43 firms for CRS and
VRS from input and output oriented point of view. We got the same results from
both the measures. In CRS efficient firms are Bajaj Hindusthan (2006,2007),
Eastern Sugar & Industries Ltd (2006,2007,2008,2009,2010), Kashipur Sugar
Mills Ltd (2010) and Piccadily Sugar & Allied Inds Ltd (2006,2010). As per VRS
efficient firms are Bajaj Hindusthan (2006, 2007,2008,2009), Balrampur Chini
(2007), Dalmia Bharat (2010), EID Parry (2009), Sh.Renuka Sugar (2010),
Eastern Sugar & Industries Ltd (2006,2007,2008,2009,2010), Kashipur Sugar
Mills Ltd (2010) and Piccadily Sugar & Allied Inds Ltd (2006, 2010). So Eastern
Sugar & Industries Ltd performs better than other DMUs from both input and
output oriented measures for last five years.
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(V)
Summary & Comments
DEA is one of the most popular techniques to assess the efficiency level
of DMUs. It is a non parametric method and need not to assume the distributional
form of the production possibility curve which gives it a comparative advantage
than other modeling techniques. Studying the exhaustive literature we found that
DEA is one of the most suitable tools to measure the efficiency of various DMUs
and no study has been done in the context of Indian steel industry in post-
liberalization era which motivates us to initiate the study.
Empirical analysis using the panel data of five years (2006-2010) from 17
Indian steel firms demonstrates that Indian firms have achieved, on an average
technical efficiency, about 86-90 per cent. From both input and output orientation
industry efficiency average in CRS is same while its different for VRS and
showing better efficiency in case of output orientation. From the study we find
that the Government owned Steel Authority of India is less efficient than Tata
Steel Ltd. which is a non-government industrial house.
References:
1) Subhash C. Ray & Hiung Joon Kim (1995): Cost efficiency in the US steelindustry: A nonparametric analysis using data envelopment analysis,European Jurnal of Operational Research, Vol.80, pp.654-671.
2) Jinlong Ma et. al.(2002): Technical efficiency and productivity change ofChinas iron and steel industry, International Journal ProductionEconomics, Vol. 76 , pp. 293312.
3) Yanrui Wu (1995): The productive efficiency of Chinese iron and steelfirms: A stochastic frontier analysis, Resource Policy, Vol.21, No.3, pp.215-222.
4) Raghbendra Jha et.al.(1991): Cost structure of Indias iron and steelindustry: Allocative efficiency, economies of scale and biased technicalprogress, Resource Policy, pp-21-30.
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5) Moneer Aalam (1986): Technical Efficiency in Indian ManufacturingIndustries: An Analysis by Establishment Size, Socio-Economic PlanningScience, Vol.20, No.4, pp. 253-260.
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Estimating Technical and Scale Inefficiencies in Data EnvelopmentAnalysis., Management Science 30, pp 1078-92.12) Coelli, T. A. (1998): Guide to DEAP Version 2-1: A Data Envelopment
Analysis (Computer) Program, Working Paper 96/08, CEPA, UNE,Australia.
13) Worthington, A.C. (1999): Measuring Technical Efficiency in AustralianCredit Unions. The Manchester School, Vo. 67, No.2.
14) Shiu, A. (2002): Efficiency of Chinese Enterprises. The Journal ofProductivity Analysis, Vol. 8(3): pp. 255-267.
15) Banker, R. D. and Thrall, R. M. (1992): Estimation of Returns to scaleUsing Data Envelopment Analysis, European Journal of OperationalResearch, Vol.62, pp.74-84
16) Topuz, J. C.et.al. (2005): Technical, Allocative and Scale Efficiencies ofREITs: An Empirical Inquiry, Journal of Business Finance & Accounting,Vol.32, No.9.
17) Sachdeva, Kapil (2010): Steel Industry: Expect Uncertainty, CareRatings Professional Risk Opinion.
18) Feroz, E.H., Kim, S. and Raab, R.L. (2003). Financial Statement Analysis:A Data Envelopment Analysis Approach. Journal of Operational ResearchSociety. Vol. 54, pp.48-58.
Annexure-1
SLNO
DMU Input Oriented Output Oriented
Year CRS VRS SCALE CRS VRS SCALE
1 Bhushan Steel
2010 0.802 0.802 1 - 0.802 0.802 1 -
2009 0.816 0.816 1 - 0.816 0.816 1 -
2008 0.76 0.76 1 - 0.76 0.76 1 -
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2007 0.783 0.805 0.973 drs 0.783 0.808 0.97 drs
2006 0.789 0.815 0.969 drs 0.789 0.818 0.965 drs
2 ISMT Ltd
2010 0.813 0.813 1 - 0.813 0.849 0.958 drs
2009 0.833 0.833 1 - 0.833 0.833 1 -
2008 0.813 0.813 1 - 0.813 0.861 0.944 drs
2007 0.903 0.903 1 - 0.903 0.915 0.986 drs
2006 0.859 0.859 1 - 0.859 0.916 0.937 drs
3 Ispat Industries Ltd
2010 0.795 0.973 0.817 drs 0.795 0.975 0.815 drs
2009 0.807 0.824 0.979 drs 0.807 0.832 0.969 drs
2008 0.822 0.841 0.978 drs 0.822 0.842 0.977 drs
2007 0.811 0.811 1 - 0.811 0.811 1 -
2006 0.669 0.669 1 - 0.669 0.669 1 -
4 Jindal Saw Ltd
2010 0.966 0.975 0.991 drs 0.966 0.975 0.991 drs
2009 0.827 0.837 0.988 drs 0.827 0.84 0.985 drs
2008 0.995 1 0.995 drs 0.995 1 0.995 drs
2007 0.806 0.806 0.999 drs 0.806 0.807 0.998 drs
2006 0.779 0.785 0.993 drs 0.779 0.79 0.987 drs
5 JSW Steel Ltd
2010 0.91 1 0.91 drs 0.91 1 0.91 drs
2009 0.882 0.996 0.886 drs 0.882 0.997 0.885 drs
2008 0.976 1 0.976 drs 0.976 1 0.976 drs
2007 1 1 1 - 1 1 1 -
2006 0.922 0.922 1 - 0.922 0.922 1 -
6Lloyds SteelIndustries Ltd
2010 0.862 0.937 0.919 drs 0.862 0.951 0.906 drs
2009 0.812 0.866 0.937 drs 0.812 0.886 0.916 drs2008 0.827 0.865 0.956 drs 0.827 0.876 0.944 drs
2007 0.762 0.763 0.999 drs 0.762 0.767 0.994 drs
2006 0.705 0.708 0.996 drs 0.705 0.718 0.982 drs
7Mahindra UgineSteel Firm Ltd
2010 1 1 1 - 1 1 1 -
2009 1 1 1 - 1 1 1 -
2008 0.889 0.964 0.922 drs 0.889 0.977 0.91 drs
2007 0.918 0.924 0.994 drs 0.918 0.962 0.955 drs
2006 1 1 1 - 1 1 1 -
8 Mukand Ltd
2010 0.771 0.771 1 - 0.771 0.774 0.995 drs
2009 0.73 0.742 0.984 drs 0.73 0.748 0.976 drs
2008 0.74 0.749 0.988 drs 0.74 0.755 0.98 drs
2007 0.784 0.798 0.983 drs 0.784 0.803 0.976 drs
2006 0.808 0.808 0.999 drs 0.808 0.814 0.992 drs
9National Steel &Agro Industries Ltd
2010 0.966 1 0.966 drs 0.966 1 0.966 drs
2009 1 1 1 - 1 1 1 -
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2008 0.93 0.962 0.966 drs 0.93 0.97 0.958 drs
2007 0.951 0.973 0.977 drs 0.951 0.981 0.969 drs
2006 0.992 1 0.992 drs 0.992 1 0.992 drs
10 RamsarupIndustries Ltd
2010 0.838 1 0.838 drs 0.838 1 0.838 drs
2009 0.867 1 0.867 drs 0.867 1 0.867 drs
2008 0.789 0.835 0.945 drs 0.789 0.842 0.937 drs
2007 0.915 0.986 0.928 drs 0.915 0.993 0.921 drs
2006 1 1 1 - 1 1 1 -
11Steel Authority ofIndia Ltd
2010 0.871 1 0.871 drs 0.871 1 0.871 drs
2009 0.821 1 0.821 drs 0.821 1 0.821 drs
2008 0.945 1 0.945 drs 0.945 1 0.945 drs
2007 0.931 1 0.931 drs 0.931 1 0.931 drs
2006 0.838 0.934 0.898 drs 0.838 0.94 0.892 drs
12 Shah Alloys Ltd
2010 0.71 0.71 1 - 0.71 0.783 0.907 drs
2009 0.679 0.679 1 - 0.679 0.733 0.926 drs
2008 0.71 0.718 0.99 drs 0.71 0.734 0.967 drs
2007 0.824 0.834 0.988 drs 0.824 0.864 0.954 drs
2006 0.785 0.792 0.992 drs 0.785 0.888 0.885 drs
13Sunflag Iron &Steel Firm Ltd
2010 0.94 1 0.94 drs 0.94 1 0.94 drs
2009 0.92 0.984 0.935 drs 0.92 0.986 0.933 drs
2008 0.916 0.993 0.923 drs 0.916 0.994 0.922 drs
2007 0.901 0.914 0.987 drs 0.901 0.935 0.964 drs
2006 0.911 0.953 0.955 drs 0.911 0.973 0.936 drs
14 Surya Roshni Ltd
2010 0.946 0.946 1 - 0.946 0.948 0.998 drs2009 0.965 0.969 0.996 drs 0.965 0.977 0.987 drs
2008 0.956 0.956 1 - 0.956 0.965 0.99 drs
2007 0.946 0.946 1 - 0.946 0.948 0.998 drs
2006 0.967 0.967 1 - 0.967 0.967 1 -
15 Tata Steel Ltd
2010 0.951 1 0.951 drs 0.951 1 0.951 drs
2009 0.959 1 0.959 drs 0.959 1 0.959 drs
2008 1 1 1 - 1 1 1 -
2007 0.98 1 0.98 drs 0.98 1 0.98 drs
2006 1 1 1 - 1 1 1 -
16 Usha Martin Ltd
2010 0.709 0.709 1 - 0.709 0.709 1 -
2009 0.82 0.82 1 - 0.82 0.82 1 -
2008 0.788 0.788 1 - 0.788 0.791 0.997 drs
2007 0.792 0.792 1 - 0.792 0.794 0.997 drs
2006 0.81 0.81 1 - 0.81 0.811 0.998 drs
17 Uttam Galva Steels 2010 0.839 0.882 0.951 drs 0.839 0.884 0.949 drs
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Ltd 2009 0.865 0.887 0.975 drs 0.865 0.889 0.973 drs
2008 0.815 0.835 0.977 drs 0.815 0.839 0.972 drs
2007 0.842 0.849 0.991 drs 0.842 0.853 0.987 drs
2006 0.822 0.822 1 - 0.822 0.822 1 -
Annexure-2
Input Oriented Output Oriented
SLNO DMUS Year CRS VRS SCALE CRS VRS SCALE
1Bajaj
Hindusthan
2010 0.171 0.615 0.279 drs 0.171 0.966 0.177 drs
2009 0.193 1 0.193 drs 0.193 1 0.193 drs
2008 0.28 1 0.28 drs 0.28 1 0.28 drs
2007 1 1 1 - 1 1 1 -
2006 1 1 1 - 1 1 1 -
2 Balrampur Chini
2010 0.102 0.603 0.169 drs 0.102 0.909 0.112 drs
2009 0.075 0.131 0.575 drs 0.075 0.753 0.1 drs
2008 0.074 0.113 0.658 drs 0.074 0.737 0.101 drs
2007 0.132 1 0.132 drs 0.132 1 0.132 drs2006 0.097 0.097 1 - 0.097 0.552 0.175 drs
3 Dalmia Bharat
2010 0.073 1 0.073 drs 0.073 1 0.073 drs
2009 0.072 0.482 0.15 drs 0.072 0.903 0.08 drs
2008 0.091 0.569 0.159 drs 0.091 0.903 0.1 drs
2007 0.099 0.179 0.553 drs 0.099 0.681 0.145 drs
2006 0.09 0.09 1 - 0.09 0.409 0.22 drs
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4 Dhampur Sugar
2010 0.085 0.085 1 - 0.085 0.521 0.162 drs
2009 0.064 0.064 1 - 0.064 0.371 0.174 drs
2008 0.059 0.059 1 - 0.059 0.332 0.177 drs
2007 0.128 0.163 0.788 drs 0.128 0.597 0.215 drs
2006 0.138 0.138 1 - 0.138 0.467 0.296 drs
5 EID Parry
2010 0.085 0.166 0.512 drs 0.085 0.69 0.123 drs
2009 0.281 1 0.281 drs 0.281 1 0.281 drs
2008 0.067 0.067 1 - 0.067 0.355 0.19 drs
2007 0.092 0.094 0.976 drs 0.092 0.425 0.217 drs
2006 0.115 0.126 0.911 drs 0.115 0.575 0.2 drs
6 Sakthi Sugars
2010 0.087 0.145 0.597 drs 0.087 0.711 0.122 drs
2009 0.083 0.094 0.89 drs 0.083 0.553 0.15 drs
2008 0.077 0.077 1 - 0.077 0.423 0.182 drs
2007 0.094 0.094 1 - 0.094 0.524 0.179 drs
2006 0.081 0.081 1 - 0.081 0.369 0.219 drs
7Sh.Renuka
Sugar
2010 0.078 1 0.078 drs 0.078 1 0.078 drs
2009 0.121 0.812 0.149 drs 0.121 0.944 0.128 drs
2008 0.092 0.092 1 - 0.092 0.435 0.211 drs
2007 0.143 0.143 1 - 0.143 0.514 0.279 drs
2006 0.245 0.245 1 - 0.245 0.451 0.543 drs
8 Triven.Engg.Ind.
2010 0.11 0.848 0.13 drs 0.11 0.975 0.113 drs
2009 0.087 0.32 0.272 drs 0.087 0.797 0.109 drs
2008 0.109 0.832 0.131 drs 0.109 0.964 0.113 drs
2007 0.121 0.192 0.628 drs 0.121 0.695 0.174 drs2006 0.133 0.165 0.804 drs 0.133 0.594 0.224 drs
9SimbhaoliSugars Ltd
2010 0.107 0.178 0.602 drs 0.107 0.701 0.153 drs
2009 0.075 0.075 1 - 0.075 0.428 0.175 drs
2008 0.069 0.069 1 - 0.069 0.268 0.258 drs
2007 0.101 0.101 1 - 0.101 0.41 0.247 drs
2006 0.088 0.088 1 - 0.088 0.313 0.28 drs
10Bannari
Amm.Sugar
2010 0.109 0.109 0.998 - 0.109 0.574 0.19 drs
2009 0.111 0.112 0.995 drs 0.111 0.497 0.223 drs
2008 0.071 0.071 1 - 0.071 0.342 0.207 drs
2007 0.123 0.123 1 - 0.123 0.523 0.236 drs
2006 0.123 0.123 1 - 0.123 0.457 0.269 drs
11DCM Shriram
Inds
2010 0.138 0.138 1 - 0.138 0.531 0.26 drs
2009 0.149 0.149 1 - 0.149 0.518 0.288 drs
2008 0.11 0.11 1 - 0.11 0.36 0.307 drs
2007 0.149 0.149 1 - 0.149 0.399 0.373 drs
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2006 0.193 0.193 1 - 0.193 0.474 0.407 drs
12 Dharani Sugars
2010 0.077 0.077 1 - 0.077 0.37 0.208 drs
2009 0.105 0.105 1 - 0.105 0.269 0.389 drs
2008 0.152 0.152 1 - 0.152 0.284 0.535 drs
2007 0.204 0.204 1 - 0.204 0.38 0.536 drs2006 0.204 0.204 1 - 0.204 0.353 0.578 drs
13 Jeypore Sug.Co
2010 0.13 0.13 1 - 0.13 0.354 0.369 drs
2009 0.116 0.116 1 - 0.116 0.286 0.406 drs
2008 0.092 0.092 1 - 0.092 0.262 0.349 drs
2007 0.136 0.136 1 - 0.136 0.323 0.423 drs
2006 0.21 0.21 1 - 0.21 0.423 0.497 drs
14 JK Sugar
2010 0.362 0.362 1 - 0.362 0.381 0.951 drs
2009 0.335 0.335 1 - 0.335 0.348 0.962 drs
2008 0.275 0.275 1 - 0.275 0.328 0.838 drs
2007 0.322 0.322 1 - 0.322 0.353 0.912 drs
2006 0.329 0.329 1 - 0.329 0.367 0.897 drs
15Kesar
Enterprise
2010 0.121 0.121 1 - 0.121 0.304 0.399 drs
2009 0.16 0.16 1 - 0.16 0.338 0.473 drs
2008 0.212 0.212 1 - 0.212 0.332 0.638 drs
2007 0.185 0.185 1 - 0.185 0.25 0.741 drs
2006 0.299 0.299 1 - 0.299 0.387 0.774 drs
16 Kothari Sugars
2010 0.156 0.156 1 - 0.156 0.332 0.468 drs
2009 0.112 0.112 1 - 0.112 0.293 0.383 drs
2008 0.131 0.131 1 - 0.131 0.324 0.404 drs2007 0.165 0.165 1 - 0.165 0.361 0.457 drs
2006 0.265 0.265 1 - 0.265 0.431 0.616 drs
17 Parrys Sugar
2010 0.149 0.149 1 - 0.149 0.158 0.943 drs
2009 0.143 0.143 1 - 0.143 0.301 0.475 drs
2008 0.267 0.267 1 - 0.267 0.581 0.459 drs
2007 0.259 0.259 1 - 0.259 0.371 0.697 drs
2006 0.158 0.158 1 - 0.158 0.374 0.422 drs
18 PonniSug.Erode
2010 0.323 0.323 1 - 0.323 0.497 0.649 drs
2009 0.412 0.412 1 - 0.412 0.43 0.958 drs
2008 0.334 0.334 1 - 0.334 0.357 0.936 drs
2007 0.429 0.429 1 - 0.429 0.429 1 -
2006 0.477 0.477 1 - 0.477 0.492 0.969 drs
19RajshreeSugars
2010 0.098 0.098 1 - 0.098 0.424 0.232 drs
2009 0.096 0.096 1 - 0.096 0.313 0.306 drs
2008 0.082 0.082 1 - 0.082 0.238 0.344 drs
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2007 0.111 0.111 1 - 0.111 0.337 0.33 drs
2006 0.175 0.175 1 - 0.175 0.411 0.426 drs
20 Thiru Aroor. Su.
2010 0.109 0.109 1 - 0.109 0.395 0.275 drs
2009 0.124 0.124 1 - 0.124 0.293 0.424 drs
2008 0.112 0.112 1 - 0.112 0.24 0.464 drs2007 0.121 0.121 1 - 0.121 0.322 0.376 drs
2006 0.109 0.109 1 - 0.109 0.304 0.359 drs
21Ugar Sugar
Works
2010 0.091 0.091 1 - 0.091 0.29 0.313 drs
2009 0.094 0.094 1 - 0.094 0.29 0.326 drs
2008 0.104 0.104 1 - 0.104 0.299 0.347 drs
2007 0.081 0.081 1 - 0.081 0.286 0.282 drs
2006 0.127 0.127 1 - 0.127 0.303 0.419 drs
22
Dwarikesh
SugarIndustries Ltd
2010 0.099 0.099 1 - 0.099 0.347 0.285 drs
2009 0.049 0.049 1 - 0.049 0.193 0.256 drs
2008 0.067 0.067 1 - 0.067 0.254 0.265 drs
2007 0.163 0.163 1 - 0.163 0.366 0.444 drs
2006 0.273 0.273 1 - 0.273 0.431 0.633 drs
23Eastern Sugar &
Industries Ltd
2010 1 1 1 - 1 1 1 -
2009 1 1 1 - 1 1 1 -
2008 1 1 1 - 1 1 1 -
2007 1 1 1 - 1 1 1 -
2006 1 1 1 - 1 1 1 -
24Empee Sugars
& Chemicals Ltd
2010 0.181 0.181 1 - 0.181 0.301 0.6 drs
2009 0.323 0.323 1 - 0.323 0.335 0.964 drs2008 0.421 0.421 1 - 0.421 0.421 1 -
2007 0.512 0.512 1 - 0.512 0.512 1 -
2006 0.78 0.78 1 - 0.78 0.783 0.997 drs
25Gayatri Sugars
Ltd
2010 0.722 0.722 1 - 0.722 0.725 0.997 drs
2009 0.311 0.311 1 - 0.311 0.311 1 -
2008 0.272 0.272 1 - 0.272 0.314 0.866 drs
2007 0.302 0.302 1 - 0.302 0.363 0.833 drs
2006 0.411 0.411 1 - 0.411 0.413 0.995 drs
26Gobind Sugar
Mills Ltd
2010 0.333 0.333 1 - 0.333 0.393 0.848 drs
2009 0.191 0.191 1 - 0.191 0.295 0.647 drs
2008 0.187 0.187 1 - 0.187 0.281 0.666 drs
2007 0.362 0.362 0.999 - 0.362 0.475 0.761 drs
2006 0.258 0.258 1 - 0.258 0.344 0.749 drs
27Indian Sucrose
Ltd2010 0.216 0.216 1 - 0.216 0.328 0.659 drs
2009 0.245 0.245 1 - 0.245 0.343 0.715 drs
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SugarCorporation Ltd
2009 0.459 0.459 1 - 0.459 0.459 1 -
2008 0.4 0.4 1 - 0.4 0.4 1 -
2007 0.419 0.419 1 - 0.419 0.422 0.992 drs
2006 0.672 0.672 1 - 0.672 0.705 0.953 drs
36Rana Sugars
Ltd
2010 0.082 0.082 1 - 0.082 0.406 0.203 drs2009 0.041 0.041 1 - 0.041 0.129 0.313 drs
2008 0.073 0.073 1 - 0.073 0.198 0.369 drs
2007 0.26 0.26 1 - 0.26 0.38 0.684 drs
2006 0.307 0.307 1 - 0.307 0.398 0.77 drs
37 SBEC Sugar Ltd
2010 0.135 0.135 1 - 0.135 0.32 0.423 drs
2009 0.229 0.229 1 - 0.229 0.361 0.635 drs
2008 0.152 0.152 1 - 0.152 0.249 0.611 drs
2007 0.306 0.306 1 - 0.306 0.379 0.806 drs
2006 0.357 0.357 1 - 0.357 0.369 0.97 drs
38Sri
ChamundeswariSugars Ltd
2010 0.135 0.135 1 - 0.135 0.332 0.406 drs
2009 0.143 0.143 1 - 0.143 0.28 0.509 drs
2008 0.148 0.148 1 - 0.148 0.285 0.518 drs
2007 0.195 0.195 1 - 0.195 0.355 0.55 drs
2006 0.306 0.306 1 - 0.306 0.399 0.768 drs
39United
ProvincesSugar Co Ltd
2010 0.224 0.224 1 - 0.224 0.345 0.648 drs
2009 0.3 0.3 1 - 0.3 0.351 0.856 drs
2008 0.26 0.26 1 - 0.26 0.347 0.751 drs
2007 0.26 0.26 1 - 0.26 0.337 0.773 drs
2006 0.293 0.293 1 - 0.293 0.359 0.816 drs
40Upper Ganges
Sugar &Industries Ltd
2010 0.066 0.066 1 - 0.066 0.254 0.262 drs
2009 0.097 0.097 1 - 0.097 0.289 0.338 drs
2008 0.063 0.063 1 - 0.063 0.243 0.26 drs
2007 0.065 0.065 1 - 0.065 0.209 0.313 drs
2006 0.157 0.157 1 - 0.157 0.386 0.406 drs
41Uttam Sugar
Mills Ltd
2010 0.072 0.072 1 - 0.072 0.295 0.244 drs
2009 0.066 0.066 1 - 0.066 0.236 0.279 drs
2008 0.062 0.062 1 - 0.062 0.183 0.341 drs
2007 0.139 0.139 1 - 0.139 0.354 0.392 drs
2006 0.266 0.266 1 - 0.266 0.425 0.625 drs
42Vishnu Sugar
Mills Ltd
2010 0.57 0.57 1 - 0.57 0.603 0.945 drs
2009 0.619 0.619 1 - 0.619 0.619 1 -
2008 0.525 0.525 1 - 0.525 0.537 0.979 drs
2007 0.435 0.435 1 - 0.435 0.461 0.943 drs
2006 0.484 0.484 1 - 0.484 0.512 0.946 drs
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43Piccadily Sugar
& Allied IndsLtd
2010 1 1 1 - 1 1 1 -
2009 0.922 0.922 1 - 0.922 0.922 0.999 drs
2008 0.746 0.746 1 - 0.746 0.751 0.993 drs
2007 0.627 0.627 1 - 0.627 0.635 0.987 drs
2006 1 1 1 - 1 1 1 -