efficiency of steel comp

7/28/2019 Efficiency of Steel Comp

1/26Electronic copy available at: http://ssrn.com/abstract=1866188

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]


2/26Electronic copy available at: http://ssrn.com/abstract=1866188

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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4

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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6

(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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16

(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.

6) Farrel, Michael J. (1957): Measurement of Productive Efficiency, Journalof Royal Statistical Society, Series A, General, 120(3), pp. 253-82.

7) Fare R. and C.A.K. Lovell (1978): Measuring the Technical Efficiency ofProduction, Journal of Economic Theory, Vol. 19, pp. 150-162.8) Meeusen, W., and J. Van den Broeck (1977): Efficiency Estimation from

Cobb-Douglas Production Function with Composed Error, InternationalEconomics Review 18, pp 435-44.

9) Charnes, A., Cooper, W.W. and Rhodes, W.E. (1978): Measuring theEfficiency of Decision Making Units., European Journal of OperationalResearch, Vol. 2, pp. 429-444.

10) Fare, R., S. Grosskpf and Lovell, C. A. K. (1985): The Measurement ofEfficiency of Production. Kluwer-Nijhoff Publishing, Boston.

11) Banker, R. D., A. Charnes and W. W. Cooper(1984): Some Models for

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.

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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

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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 -

efficiency of steel comp

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