PERFORMANCE ANALYSIS OF STEEL COMPANIES USING ERM-DT
DISSERTATION DRAFT II
RASHMI NANDANWAR 13020241040 2/10/2015
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ABSTRACT
Organizations often face complex situations while selecting their business partners such as
vendors. Identification, selection and evaluation of the best possible option available involve
financial as well as time investment in addition to added risks. This article describes the
typical steps of performance evaluation processes: identifying relevant organizations,
soliciting information and employing MCDM techniques. The MCDM techniques used are
DEA and TOPSIS, their advantages over conventional research methodologies, comparative
analysis between DEA and TOPSIS and the necessity of ERM-DT for an inclusive conclusion.
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TABLE OF CONTENTS
Abstract
1
Introduction
3
Literature Review
4
Objectives
7
Methodology
7
Analysis and Conclusion
14
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INTRODUCTION
PRESENT SCENARIO
The steel industry has been supporting and promoting the development of other
industries such as industry, agriculture, transportation, etc. The products produced by
these companies are raw materials to other manufacturers such as auto makers, home
appliance manufacturers, construction companies, ship builders, etc. Steel products are
commoditized, and, as a result, competition is fierce in the market. In addition, the
demands of these products are closely tied to the economy of the world. The steel
manufacturing industry has touch competition among global steel manufacturers.
Information such as market share, competitive pricing, production technology, or
service quality is not transparent or not readily available for raw-material (iron ores)
suppliers and buyers (end-product manufacturers) when they are to evaluate the
performances of the steel manufacturers to make strategic alliancing decisions.
Accordingly, the performance benchmarking of these companies can be an interesting
research topic to related firms and practitioners in steel company.
In this paper, the method of Multiple Criteria Decision Analysis is applied to assess the
performance of the listed companies of the steel industry. In Multiple Criteria Decision
Analysis DEA and TOPSIS methods are to be implemented. Data envelopment analysis is
a linear programming application used to evaluate efficiency of a number of producers
or Decision Making Unit. DEA compares each DMU with only the best DMU.
TOPSIS selects the alternative that is the closest to the ideal solution and farthest from
negative ideal solution.
To carry out the analysis the Steel companies were first identified and then the input
and output parameters for DEA and TOPSIS for the steel companies were identified.
Then, the performance evaluation is done. To exploit the advantages of both the
techniques, a collaborative analysis known as ERM-DT is done. Comparison and analysis
of both the MCDM and ERM-DT is done subsequently to arrive at a conclusion.
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LITERATURE REVIEW
PRESENT METHODOLOGIES
K.S. Kavitha and Dr. P. Palanivelu (2014) conducted a study of Financial Performance of
Iron and Steel Industries India to investigate the factors affecting the industry based on
profitability Model by implementing analytical research designs mainly ANOVA.
Shrabanti Pal (2012) conducted a comparative study of Financial Performance of Indian
Steel Companies under Globalization. This study used Multiple regression analysis on
fifteen financial ratios (variables) selected from different segment like liquidity,
solvency, activity and profitability such as current ratio, quick ratio, absolute quick
ratio, interest coverage ratio, debt-equity ratio, raw material turnover ratio, work in
progress turnover ratio, finished goods turnover ratio, fixed assets turnover ratio, sales
to compensation ratio, sales to raw materials and stores expenses ratio, sales to selling
and distribution expenses ratio, sales to technical knowhow expenses and return on
investment ratio selected from liquidity, leverage, efficiency and profitability category
to reveal the linear relationship between them and also to discover the
variable/variables which mostly influence the overall profitability of the company.
A study has been conducted by Bhunia (2010) on private sector steel companies of
India to test the short term liquidity trend of the companies and its effect on the
financial performance. The study reveals that the inventory and receivable management
require special attention and proper control over inventory. The investment in loans
and advances should be minimized to the extent possible. A balanced and proper
amount of working capital should be maintained in the business for smooth running of
the same. The management of the companies should adopt a viable and proficient
payment policy. At the same time maximization of assets and minimization of liabilities
should be preserved and help Indian steel companies to grow further. A proper working
capital management system ensures the hazard free business operations and also
enhances the profitability of the company.
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CHALLENGES
Since the initial development of DEA, although a number of studies have been
published exploring both the theory and applications of the technique in the
public and private sectors (e.g. Banker et al., 1984; Chalos and Che-rian, 1995;
Karkazis and Thanassoulis, 1998; Wang et al.,2001), there have been relatively
few applications related to the performance of the steel industry (few exceptions
are Gruver and Y u, 1985; Ray and Kim, 1995; Zhang and Zhang, 2001; Ma, Evans,
Fuller and Stewart, 2002, etc.). In contrast to prior performance studies focusing on
financial accounts, DEA can be used as an effective tech-nique underlying the
nonparametric frontier approach. Each year of the steel industry is compared to
the ‘model’ frontier and the closer a year gets to the best frontier, the more the
industry has been successful in ‘catching-up’ and this is due to better use of technology
and equipments. The frontier version of productivity change consists of the ‘catching
up’ and technical change experienced by the steel industry . The longitudinal DEA
based on the aggre-gated industry- or country-level data might be questioned by
the existence of the long-term industry-wide best practice. However, this kind of
assumption has been seen in prior studies, such as Mahadevan’ s analysis (2002)
on the productivity growth performance of Malaysia' s manufacturing sector using
a panel data of 28 industries from 1981-1996, and Chen’ s (2003) measurement of
the productivity change of three Chinese major industries during four five-year-
plan periods. Though industrial performance can be measured collectively by the
sum of individual firms, little can be done about this when firm-level data is
fragmented and unavailable to offer a complete picture of industrial performance
across time.
Markovitz’s Portfolio theory and his mathematics and operation research in finance
sector in 1950s have been widely used (Markowitz, 1952, 1959). Since then operation
research has contributed to solving various problems in financial sector including
portfolio selection, venture capital investments, bankruptcy estimate, financial
planning, company merge and acquisitions. This contributions are not limited to
academic research, also extended to daily practices of various corporate companies.
(Constantin ve Micheal, 2002). Researchers emphasize the importance of taking various
factors into consideration during the problem solving process due to the multi-
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dimensional nature of financial decisions (Jacquillat, 1972: Zeleny, 1977, 1982: Colson
ve Zeleny, 1979; Bhaskar ve McNamee, 1983; Sponk ve Hallerbach, 1997). Multicriteria
decision analysis methodology called TOPSIS is generally used to solve problems in such
cases where there are many and mostly inconsistent criteria.
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OBJECTIVES
To identify major players in the steel industry and important attributes.
Employee Multiple Criteria Decision Analysis: DEA and TOPSIS, for performance
evaluation.
To Collaborate the DEA and TOPSIS analysis to form the ERM-DT analysis for
performance evaluation
To compare the research methodologies implemented.
METHODOLOGY
DATA ENVELOPMENT ANALYSIS
Data envelopment analysis (DEA) is a linear programming methodology to measure the
efficiency of multiple decision-making units (DMUs) when the production process
presents a structure of multiple inputs and outputs.
The DEA methodology was initiated by Charnes et al. (1978) who built on the
frontier concept pioneered by Farrell (1957). It is chosen by this study for the
following reasons: first, the DEA has some advantages over the stochastic frontier
approach which calculates both echnical efficiency and technical change
components of TFP growth (Fare el al., 1989; Chavas and Cox, 1990
"DEA has been used for both production and cost data. Utilizing the selected variables,
such as unit cost and output, DEA software searches for the points with the lowest unit
cost for any given output, connecting those points to form the efficiency frontier. Any
company not on the frontier is considered inefficient. A numerical coefficient is given to
each firm, defining its relative efficiency. Different variables that could be used to
establish the efficiency frontier are: number of employees, service quality,
environmental safety, and fuel consumption. An early survey of studies of electricity
distribution companies identified more than thirty DEA analyses—indicating
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widespread application of this technique to that network industry. (Jamasb, T. J., Pollitt,
M. G. 2001). A number of studies using this technique have been published for water
utilities. The main advantage to this method is its ability to accommodate a multiplicity
of inputs and outputs. It is also useful because it takes into consideration returns to
scale in calculating efficiency, allowing for the concept of increasing or decreasing
efficiency based on size and output levels. A drawback of this technique is that model
specification and inclusion/exclusion of variables can affect the results." (Berg 2010)
Under general DEA benchmarking, for example, "if one benchmarks the performance of
computers, it is natural to consider different features (screen size and resolution,
memory size, process speed, hard disk size, and others). One would then have to classify
these features into “inputs” and “outputs” in order to apply a proper DEA analysis.
However, these features may not actually represent inputs and outputs at all, in the
standard notion of production. In fact, if one examines the benchmarking literature,
other terms, such as “indicators”, “outcomes”, and “metrics”, are used. The issue now
becomes one of how to classify these performance measures into inputs and outputs, for
use in DEA." (Cook, Tone, and Zhu, 2014)
One important advantage of DEA is that it envelopes observed input-output data
without requiring a priori specification of functional forms. Different specifications of
the production function under the parametric approach simply represent a value
added accounting identity with little theoretical justification (Felipe, 1999).
The other advantage is that the nonparametric nature of DEA allows it to
concentrate on revealed best-practice frontiers rather than on central-tendency
properties of frontiers. Furthermore, as argued in Gong and Sickles (1992), DEA is
more appealing than the econometric model as inefficiency is likely to be
correlated with the inputs. Lastly , DEA is able to provide information on scale
efficiency without the need for price data which are difficult to obtain due to the
collective nature in an industry level study .
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Advantages of DEA:
no need to explicitly specify a mathematical form for the production function
proven to be useful in uncovering relationships that remain hidden for other
methodologies
capable of handling multiple inputs and outputs
capable of being used with any input-output measurement
the sources of inefficiency can be analysed and quantified for every evaluated
unit
However, DEA is not free from drawbacks either. These shortages include assumedly
non-existent measurement error and statistical noise, and disallowance for
statistical tests which are typical of the econometric approach.
Disadvantages of DEA:
results are sensitive to the selection of inputs and outputs (Berg 2010).
you cannot test for the best specification (Berg 2010).
the number of efficient firms on the frontier tends to increase with the number of
inputs and output variables (Berg 2010)
TECHNIQUE FOR ORDER OF PREFERENCE BY SIMILARITY TO IDEAL
SOLUTION
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a
multi-criteria decision analysis method, which was originally developed by Hwang and
Yoon in 1981 with further developments by Yoon in 1987 and Hwang, Lai and Liu in
1993. TOPSIS is based on the concept that the chosen alternative should have the
shortest geometric distance from the positive ideal solution and the longest geometric
distance from the negative ideal solution. It is a method of compensatory aggregation
that compares a set of alternatives by identifying weights for each criterion, normalising
scores for each criterion and calculating the geometric distance between each
alternative and the ideal alternative, which is the best score in each criterion. An
assumption of TOPSIS is that the criteria are monotonically increasing or decreasing.
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Normalisation is usually required as the parameters or criteria are often of incongruous
dimensions in multi-criteria problems. Compensatory methods such as TOPSIS allow
trade-offs between criteria, where a poor result in one criterion can be negated by a
good result in another criterion. This provides a more realistic form of modelling than
non-compensatory methods, which include or exclude alternative solutions based on
hard cut-offs
Topsis is the most preferable technique by the most of researhers. Wang (2008) used
the FMCDM technique to evaluate the financial performances of many airway
companies in Thailand. Khodam, Hemmati and Abdolshah (2008); Wu, Cheng-Ru, Lin,
Chin-Tsai and Pei-Hsuan (2008); Pal (2009); ibha (2011) also used TOPSIS to analyze
financial performances of companies in banking sector. Deng, Yeh and Willis (2000)
claimed that TOPSIS is the easiest technique to evaluate and analyze the performances
by using financial ratios in China. Feng and Wang (2000) prefered TOPSIS to analyze 5
Thai airway companies by using 22 variables defining transportation and financial
indicators.
In Turkey, Dumanoğlu and Ergül (2009) compared 11 technological companies traded
at IMKB in terms of performance by using TOPSIS for 4 quarters during 2006-2009
years. Besides this, they used TOPSIS and ELECTRA techniques to evaluate financial
performances of companies functioning in food sector both for the company itself and
the sector in general and they observed these 2 techniques give reliable results. Eleren
and Karagül(2008) in their studies about evaluating financial performance of Turkish
economy in general for the years 1986-2006 by TOPSIS method. By this method they
could evaluate performance according to criteria defined for each year separately and
they found out that 1986 was the best but 1999 was the worst year in terms of financial
performance.
Demireli (2010) took the advantage of TOPSIS to analyze performances of public banks
by using equally weighted financial ratios which have being most commonly used in
literature. Mangır and Erdoğan (2011) also used Fuzzy Topsis to evaluate economical
performances of 6 chosen countries during the global economic crisis period. Özgüven
(2011) again used TOPSIS to evaluate 3 businesses for the years 2005-2009, just prior
to crisis period.
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Advantages of TOPSIS
1. Easy decision making using both negative and positive criteria.
2. Number of criteria can be applied during the decision process.
3. Simple and faster than AHP, FDAHP, SAW
DATA
i. Sampling Design: In the global steel industry, there are various competitors, but the
industry is not dominated by any one steel company. From the top 30 steel-
producing companies in 2013, as per the World Steel Association, JSW, Jindal steels,
TATA Steel, SAIL and Bajaj Steel Industries were chosen. The data for these
companies agree with the DEA and TOPSIS analysis.
ii. Sources of Data: For the study, secondary data is used. The data are collected from
the audited balance sheets, profit and loss statements, magazines, journals, library
sources. For the analysis, the most recent data available, financial reports 2013 are
used.
iii. Data: For both TOPSIS and DEA, ratios are used for the analysis.
Inputs: Inventory Turnover Ratio and Total Expenditure to Sales ratio
Output: Profitability
APPLICATION OF DEA
For implementing DEA, the DEAP software is used.
Data file:
0.036 0.812 4.643
0.124 0.715 3.047
0.221 0.696 0.937
0.033 0.922 4.86
0.062 0.897 5.81
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Instruction file:
data.txt DATA FILE NAME
out.txt OUTPUT FILE NAME
5 NUMBER OF FIRMS
1 NUMBER OF TIME PERIODS
1 NUMBER OF OUTPUTS
2 NUMBER OF INPUTS
0 0=INPUT AND 1=OUTPUT ORIENTATED
0 0=CRS AND 1=VRS
0 0=DEA(MULTI-STAGE), 1=COST-DEA, 2=MALMQUIST-DEA, 3=DEA(1- STAGE), 4=DEA(2-STAGE)
Results from DEAP Version 2.1
Instruction file = ins.txt
Data file = data.txt
Input orientated DEA
Scale assumption: CRS
Slacks calculated using multi-stage method
EFFICIENCY SUMMARY:
firm te
1 0.140
2 0.546
3 1.000
4 0.113
5 0.218
mean 0.403
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APPLICATION OF TOPSIS
TOPSIS was implemented in MS Excel using the following algorithm.
step 1 Construct a Normalized Decision Vector
Step 2 Construct Weighted Normalized Decision Matrix
Step 3 Determine Ideal and Negative Solutions
Step 4 Calculate Separation Measures for each alternative
a) Separation Measure for Ideal Alternative
b) Separation Measure for Negative Alternative
Step 5 Calculate the Relative Closeness to the ideal solution
The result of TOPSIS:
JSW 0.15361304
jindal 0.161536996
tata steel 0.19305498
Bajaj Steel
Industries
0.153128675
SAIL 0.190264831
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ANALYSIS AND CONCLUSION
SR. NO. DMU
OUTPUT (PROFITABILITY)
INPUT 1 (TOTAL EXPENDITURE TO SALES RATIO)
INPUT 2 (INVENTORY TURNOVER RATIO) DEA TOPSIS
1 JSW 0.036 0.812 4.643 0.140 0.1536
2 Jindal 0.124 0.715 3.047 0.546 0.1615
3 TATA Steel 0.221 0.696 0.937 1.000 0.193
4 Bajaj Steel 0.033 0.922 4.86 0.113 0.1531
5 SAIL 0.062 0.897 5.81 0.218 0.1902
As expected, both the DEA and TOPSIS analysis has rated the DMUs from 0 to 1.
However the ratings do not agree with each other. As per DEA, the second best DMU is
Jindal, however as per TOPSIS the second best DMU is SAIL.
To reach a collaborative conclusion, ERM-DT should be implemented which combines
both DEA and TOPSIS for a more informative analysis.
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