dea and topsis analysis

PERFORMANCE ANALYSIS OF STEEL COMPANIES USING ERM-DT

DISSERTATION DRAFT II

RASHMI NANDANWAR 13020241040 2/10/2015

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.

TABLE OF CONTENTS

Abstract

1

Introduction

3

Literature Review

4

Objectives

7

Methodology

7

Analysis and Conclusion

14

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.

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.

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-

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.

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

http://en.wikipedia.org/wiki/Linear_programming

http://en.wikipedia.org/wiki/Efficiency_%28economics%29

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 .

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.

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.

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

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

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

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.

dea and topsis analysis

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