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    Measuring the Efficiency of Indian IT Industry DEA

    Analysis

    Submitted to Lovely Professional University

    In partial fulfilment of therequirements for theaward of degreeof

    MASTER OF BUSINESS ADMINISTRATION

    Submitted by:

    Harvinder Verma

    Reg. No7460070069

    Inderjeet Singh

    Reg. No7460070072

    Mnadeep Singh

    Reg. No7460070079

    Supervisor:

    Name of the Faculty Advisor

    Sukwinder Kaur

    DEPARTMENT OF MANAGEMENT LOVELY PROFESSIONAL UNIVERSITY

    PHAGWARA

    (2007-2012)

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    CERTIFICATION/THESIS APPROVAL BY FACULTY ADVISOR

    TO WHOMSOEVER IT MAY CONCERN

    This is to certify that the project report titled Measuring the Efficiency of Indian IT

    Industry: DEA Analysis carried out by Harvinder Verma, Inderjeet Singh and

    Mandeep Bhullar, have accomplished under my guidance & supervision as a duly registered

    MBA student of the Lovely Professional University, Phagwara. This project is being

    submitted by him/her in the partial fulfilment of the requirements for the award of the Master

    of Business Administration from Lovely Professional University.

    Their dissertation represents their original work and are worthy of consideration for the

    award of the degree of Master of Business Administration.

    Sukhwinder Kaur

    (Name & Signature of the Faculty Advisor)

    Date: 26/04/2012

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    DECLARATION OF AUTHENTICITY BY STUDENT

    DECLARATION

    I, Harvinder Verma, hereby declare that the work presented herein is genuine work done

    originally by me and has not been published or submitted elsewhere for the requirement of a

    degree programme. Any literature, data or works done by others and cited within this

    dissertation has been given due acknowledgement and listed in the reference section.

    Harvinder Verma

    (Student's name & Signature)

    7460070069

    (Registration No.)

    Date: 26/04/2012

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    DECLARATION OF AUTHENTICITY BY STUDENT

    DECLARATION

    I, Inderjeet Singh, hereby declare that the work presented herein is genuine work done

    originally by me and has not been published or submitted elsewhere for the requirement of a

    degree programme. Any literature, data or works done by others and cited within this

    dissertation has been given due acknowledgement and listed in the reference section.

    Inderjeet Singh

    (Student's name & Signature)

    7460070072

    (Registration No.)

    Date: 26/04/2012

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    DECLARATION OF AUTHENTICITY BY STUDENT

    DECLARATION

    I, Mandeep Singh, hereby declare that the work presented herein is genuine work done

    originally by me and has not been published or submitted elsewhere for the requirement of a

    degree programme. Any literature, data or works done by others and cited within this

    dissertation has been given due acknowledgement and listed in the reference section.

    Mandeep Singh

    (Student's name & Signature)

    7460070079

    (Registration No.)

    Date: 26/04/2012

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    vi

    Table of Contents

    Serial No. Topic Page no.

    1. Executive Summary 1-2

    2. Introduction 3-4

    3. Literature Review 5-10

    4. Need, Scope and Objectives 11-12

    4.1. Need 12

    4.2. Scope of the stody 12

    4.3. Objectives 12

    5. Research Methodology 13-15

    5.1. Research design 14

    5.2. Sampling Technique 14

    5.3. Sample Size 14-15

    6. Data Collection 16-17

    7. DEA Approach 18-26

    7.1. DEA Technique 19

    7.2. DEA Analysis 20-26

    8. Risk Factors 27-31

    9. Result 32-33

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    10. Conclusion 34-35

    11. Recommendations 36-37

    12. References 38-41

    13 Appendix 42-55

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    CHAPTER - I

    EXECUTIVE SUMMARY

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    1. Executive summary

    In the current global scenario, stock markets are tumbling down, investors are incurring losses

    and industrial growth is slowing down. In spite of this fact, Indian IT industry is reporting a net

    profit. In this study we will be calculating the efficiency of Indian IT industry with the help of DEA

    analysis. The efficiency of the industry can also be calculated with the help of ratio analysis butDEA is better tool and gives more accurate information about the efficiency of the companies. In

    DEA, we are taking sample size of 10 IT companies in India which are major key players driving

    the growth of IT industry. While applying DEA, we chose 10 DMUs which are TCS, Infosys

    technologies, Wipro, HCL, Cognizant technologies, SAP India, Oracle, Tech Mahindra, Cisco and

    Redington. The DEA showed HCL tops the list of the efficient IT companies and the efficient

    frontier consisted of TCS, Infosys technologies, HCL, Redington, Cisco, SAP India, Tech Mahindra.

    Oracle, Wipro and Cognizant were not lying on the efficient frontier. This study also analysed

    various environmental factors which affect the IT companies. Through the secondary study of

    annual reports, we find that all these companies considered the effect of economic conditions

    of the country and exchange rate fluctuations most important which can have adverse effect onthe financials of the companies. To overcome the effect of foreign exchange fluctuations risk,

    we recommend that companies can hedge the risk using forward market hedge, money market

    hedge and options market hedge.

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    CHAPTER - II

    INTRODUCTION

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    2. Introduction

    Indian IT industry is well known for cost effectiveness. The Indian information

    technology (IT) industry has played a major role in placing India on the international

    map. The industry is mainly governed by IT software and facilities for instance System

    Integration, Software experiments, Custom Application Development and Maintenance

    (CADM), network services and IT Solutions. (Mathur 2010)1

    The present study intends to analyze the performance of the Indian IT industry by

    working out the technical efficiency of the software and the telecommunication firms

    using the mathematical model of data envelopment analyses (DEA). The study also

    proposes to examines the impact of various determinants on technical efficiency of the

    software firms and net exports across IT firms further, examines the determinants for

    new technology adoption by telecommunication industry because the success of the

    software firms in terms of its exports is intertwined with the performance of the

    telecommunication industry. The study will quantify the changes needed in the relatively

    good Indian it environment and the readiness indices to increase the usage among

    individuals, businesses and the government.

    Efficiency can be framed as operating efficiency (Stiglitz, 1981). Operating efficiency

    (Farrell, 1957) denotes whether a firm is cost minimising (consuming less inputs for thesame level of outputs) or profit maximising (producing more outputs for the same amount

    of inputs) based on published accounting numbers.

    In recent time, Indian IT industry has been consistently working towards the development

    of technological changes and its usage in the banking operations for the improvement of

    their efficiency. To get the benefits of enhanced technologies, Indian ITes are

    continuously encouraging the investment in Research and Development. As a

    representative body, the national association of software services companies(NASSCOM) said recently that the IT industry would grow 13-15 percent in the ensuing

    fiscal (2012-13) after crossing the $100 billion mark in this fiscal (2011-12) with exports

    accounting for $70 billion. This double digit growth is known to be driven by cost

    effectiveness (Mathur 2010) and our paper, in nutshell, will account for various reasons

    of this growth.

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    CHAPTER -III

    LITERATURE REVIEW

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    Literature review

    Grigorian, A. David, Manole Vlad, (2002) used DEA approach to check performance

    of commercial banks in transition and used linear programming method to establish

    which banks will determine envelopment surface composed of best practice units to be

    used in DEA. The results showed that DEA can be successfully applied to banking

    systems in transition. They found that privatization of the banks, beyond those involving

    transfer of shares to the foreign owners, does not result in statistically significant

    improvement in efficiency.

    Bukri and Niazi (2003) tested the privatization effect on Pakistans banking cost and

    allocation, technical, scale and pure technical efficiency through the DEA approach and

    regression analysis of unbalanced panel data over the sample period of 1991 2000. The

    result through the DEA approach showed that foreign banks achieved the highest

    efficiency level as compared to private and public banks. However in contrast with public

    banks, private banks are more efficient.

    Beccal Elena, Casu Barbara and Girardone Claudia, (2003) measured the efficiency

    and stock performance in European Banking by employing a three-step procedure to

    generate the information required for the study. The data comprises of all the publicly

    listed banks in France (Bourse de Paris), Germany (Deutsche Brse Group), Italy (Borsa

    Italiana Spa), Spain (Bolsa de Madrid) and UK (London Stock Exchange) and data of

    stock from 03.01.2000-30.06.2001 had been taken. They found that the overall efficiency

    scores range between 70% and 90% and during the past decades, competitive pressures

    have increasingly driven banks to change their strategic focus on generating returns to

    shareholders.

    Al-Tamimi Hussein A. Hassan (2003) analyses the use of the data envelopment analysis

    (DEA) in the performance measurement of UAE commercial banks along with some

    traditional measures namely ROE, ROA, loans to deposits, and loans to total assets

    during the period 1997-2001. The main findings of the study are (i) Most of the UAE

    commercial banks appear inefficient when DEA is used. (ii) It was found that the national

    banks were more efficient than the foreign banks. This might be attributed to the fact that

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    most the ownership of these banks are belong government bodies, therefore, they have

    more facilities and they are facing less restrictions in managing their operations; (iii) two

    traditional ratios namely, loans to deposits, and loans to total assets indicate that the UAE

    commercial banks to some extent did not use the available resources properly. This

    suggests that there was an excess liquidity and therefore the UAE commercial banks

    require to develop new strategies in order to utilise the available resources.

    Feroz EH, Kim S, Raab RL, (2003) analysed the financial statements with the help of

    DEA approach taking a sample of three unrelated industries to test the null hypothesis

    that there is no relationship between DEA and traditional accounting ratios as measures

    of performance of firm. The results showed that DEA can supplement the traditional ratio

    analysis and it provides additional information than that provided by ratio analysis.

    Galagedera U.A. Don, Edirisuriya Piyadasa, (2004) measured the performance of

    Indian commercial banks(1995-2002) by application of DEA and Malmquist productive

    index. The authors have used total deposits and operating expenses as input and loans and

    other earning assets as output in the DEA analysis. The results showed that overall

    efficiency of Indian commercial banks was 0.92 and managerial efficiency was 0.96.

    They also found that managerial efficiency of public sector banks however is higher than

    private sector counterparts and also observed no significant growth in productivity during

    the sampled period.

    Bosetti Valentina, Cassinelli Mariaester and Lanza Alessandro (2004) analyze the

    performances of tourism management of local governments when economic and

    environmental aspects are considered as equally relevant. It was found that DEA analysis

    produces relative efficiency indices for each considered municipality and also gives

    useful information concerning which lever should be more effective in order to move to

    higher levels of efficiency. Data Envelopment Analysis can be effectively applied in

    assessing economic and environmental performances of tourism management. This can

    be even more useful for countries where the tourism industry has both increasing

    economic relevance and a growing impact on the environment.

    Saranga Haritha and Phani B.V., (2006) measured the internal efficiencies of the

    Indian pharmaceutical industry using a sample of 44 Pharma companies and applying

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    DEA approach. The results showed that size of a company does not dictate the internal

    efficiency ratings; however indigenous firms, which are in the business of both Bulk and

    Formulations, have an edge over MNCs and firms with only Formulations business.

    Feroz, H. Ehsan, Goel, Sanjay and Raab, L. Raymond(2006) analyze the

    pharmaceutical industry, which includes many multinational corporations with complex

    governance problems, over ten recent years, and the strategies that allowed firm

    efficiency rankings to improve or worsen over time are highlighted. Their analyses

    indicate that the inclines and declines in DEA efficiency rankings are related to the

    strategic choices made by the upper management, thereby lending credibility to the use of

    these rankings in performance measurement by the board of directors. The main findings

    of the approach are that it overcomes some of the difficult issues faced by the board of

    directors in comparing performance of the management to the best case scenario within

    the same industry

    Kuosmanen Timo and Kortelainen Mika(2006) developed a new approach for

    environmental valuation within Environmental cost-benefit analysis (ECBA) framework

    that is based on data envelopment analysis (DEA) and does not demand any price

    estimation for environmental impacts .It measures environmental costs in terms of

    absolute rather than relative shadow prices.

    Pasiouras, Fotiossifodaskalaki, Emmanouil and Zopounidis, Constantin (2007)

    follow a two-stage procedure to examine for the first time the cost efficiency of Greek

    cooperative banks. They used data envelopment analysis (DEA) to estimate the technical,

    allocate and cost efficiency for each bank in sample. Then, Tobit regression is used to

    determine the impact of internal and external factors on banks efficiency. The results of

    DEA indicate that Greek cooperative banks could improve their cost efficiency by 17.7%on average as well as that the dominant source of cost inefficiency is distributive rather

    than technical.

    Zhu Joe (2008) study the performance of airline industry in 2007 and 2008 . It was found

    that DEA model was able to evaluate the performance with respect to its fleet operation

    efficiency and performance on passenger revenue generation.

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    FAllon Cayon Edgardo and Sabogal Sarmeinto Julio (2009) measured relative

    efficiencies in the shoe industry sector in Columbia. They analyzed financial data from

    75 companies of the Columbian shoe industry to determine which factor among cost of

    capital, net operational profits after tax or invested capital in the firm, are more important

    in maximizing the EVA of the firm. They found that invested capital was important

    factor in making the EVA positive for the firm for the years 2006-07 and Net operational

    profits after tax was main factor for the years 2005-04. They also found that DEA can be

    used to measure the relative performance specific firms that operate in the common

    economic sectors.

    Babalos Vassilios, Caporale,MariaGuglielmo and Philippas Nikolas (2009) evaluated

    and assessed relative performance of Greek Equity funds using DEA. They studied the

    effect of cost and operational attributes on the operational efficiency of funds and used

    risk-adjusted returns, Jenson alpha and Carhart as the output variables. They found that

    there is negative relationship between fund performance and assets under management.

    For this result they said that structure of the domestic market may be reason for this

    negative relationship.

    Dash Mihir, Charles Christable, (2009) studied technical efficiency of banks in India

    and determined tight inputs and outputs for the banks using DEA model. The authors

    took sample of 49 major banks operating in India, of which 20 were public sector banks,

    19 were private sector banks and 10 were foreign banks. Further in DEA model five input

    variables namely, borrowing, deposits, fixed assets, net worth and operating expenses,

    and four output variables namely, advances & loans, investments, net interest income and

    non-interest income were used. They found that foreign banks were more efficient that

    public and private banks and that there was not much difference between efficiency of

    public and private banks. Further, they found that there were some significant differences

    in terms of utilization of inputs and under-production of outputs.

    Tripathy Ishita, Yadav Surendra and Sharma Seema, (2009) measured the Efficiency

    of pharmaceutical firms in India using a two stage DEA framework and data of about 300

    large pharamaceutical firms. The results showed that the performance of a large number

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    of sample firms was sub-optimal, ranging between 68% and 78% and these firms need to

    reduce their inputs to attain a given level of output to become efficient.

    Rajput Namita and Gupta Monika, (2011) assesed the efficiency and profitability of

    Indian commercial banks and analysed the role of Information Technology and its

    relevancy in Indian banks in the recent era using the data of 86 banks and applying DEA

    approach. They found that there is an increasing trend in performance of Indian banks

    caused by IT innovation and enlarged investment in new information technology during

    the recent time period (2005-06 to 2009-10).

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    CHAPTER IV

    NEED, SCOPE AND OBJECTIVES

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    4.1. Need

    In the present scenario as the global market is tumbling at greater pace but still Indian IT

    sector is performing relatively well. For instance, Infosys managed to earn good net

    profits even when the stock indices was having bearish run (Infosys results: Q2 net profit

    at Rs 1906 cr for FY 2011, up 11%). Moreover, (NASSCOM) said recently that the IT

    industry would grow 13-15 percent in the ensuing fiscal (2012-13) after crossing the

    $100 billion mark in this fiscal (2011-12) with exports accounting for $70 billion. So a

    need arises to know the various factors which integrates together to drive the growth of

    Indian IT industry.

    4.2. Scope of the study

    The study will take into consideration major IT sectors players with revenue for FY 2011

    more than Rs. 4000 crores and double digit growth. So our study will be limited to these

    companies which reflect the ongoing trend in the Indian IT industry. All the companies

    are not domestic IT industries, some of them like Accenture India which is international

    player, has also been taken into consideration. All the companies are registered in India

    but some of them are not listed in India. So it will not be a level playing field in terms of

    investment made or location from which funds are raised. So our scope is not limited

    only to domestic players. Further, SMES of IT industries are not taken into research

    because our criterion is more than 4000 crores..

    4.3. Objectives

    To measure the efficiency of Indian IT sector. To study the various factors affecting the efficiency of IT industry in India.

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    CHAPTER-IV

    RESEARCH METHODOLOGY

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    Research Methodology

    3.1 Research Design

    In our research we undertake descriptive research design. Our first objective is to find

    efficiency of IT companies and then to find various factors affecting it. So we know our

    underlying problem (to calculate efficiency), and solution will be provided by descriptive

    research.

    3.2 Sampling technique

    Sampling technique used is non probability type judgemental sampling. A criterion is

    listed down and companies are selected from that based on our judgement. Based on our

    criteria, top IT Companies with minimum revenue for FY 2011 to be Rs. 4000 Cr and

    double digit growth (i.e. 10% or more) in revenue from FY 2010 to FY 2011 are selected.

    Out of the total registered companies in India, only seventeen companies (see appendix 1)

    meets the above criterion. The top 20 companies have contributed over 64 per cent to the

    combined revenue, according to a study by CyberMedia's Dataquest Research. So this

    sample represents considerable market share.

    3.3 Sample size

    Seventeen IT companies have been taken as a sample size based on above mentioned

    criterion. List is given below (*see appendix 1):

    Company Name and Rank*

    1. Tata Consultancy services (TCS)

    2. Infosys technologies

    3. Wipro

    4. Cognizant technology solutions

    5. HCL technologies

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    6. Redington India

    7. Cisco Systems India

    8. Oracle India

    9. Accenture india

    10. Tech mahindra

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    CHAPTER V

    DATA COLLECTION

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    Data collection

    Data is collected from secondary sources like web sites related to IT industry, company

    finances (moneycontrol.com) and also from various case studies and articles (Nasscom

    stats). Financial data will be collected for a period of past 2 years (FY 2010-FY2011).

    First source will be companies own websites. For financial data, audited annual reports

    will be taken from rediff money, money control, BSE etc. Analysis of the data will be

    based upon DEA technique.

    After choosing sample size, the most significant task is to define the input and output

    variable for analysis and data to be collected for it. There is a tradeoff to be made

    between number of variables and accuracy. In nutshell, DEA approach should not use

    more than five variables per firm in Input oriented analysis. Five variables should as a

    whole should represent whole cost and inputs which can have effect on output. Following

    five variables are used (see appendix 2)

    Xi = Input variables

    X1 = No. of employees: proxy for skilled manpower, manpower expenses and

    investment in human resource

    X2 = Total Assets: A direct input to produce output, proxy for size of firm.

    X3 = Total operating expenses: cost analysis for producing outcome

    Y1 = output variable = Total Sales revenue

    (See appendix)

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    CHAPTER VI

    DEA APPROACH

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    5.1 Analysis technique DEA Approach

    DEA is a linear programming model used to measure technical efficiency. It comes up

    with a single scalar value as a measure of efficiency. Efficiency of any firm can be

    defined in terms of either output maximization for a set of inputs or input minimization

    for a given output. In DEA, relative efficiencies of a set of decision-making units

    (DMUs) are calculated. Each DMU is assigned the highest possible efficiency score by

    optimally weighing the inputs and outputs. DEA constructs an efficient frontier

    composed of those firms that consume as little input as possible while producing as much

    output as possible. Those firms that comprise the frontier are efficient, while those firms

    below the efficient frontier are inefficient.

    Data envelopment analysis offers several characteristics that are quite unique and useful

    in comparison to traditional financial analysis methods like ratio analysis or regression

    analysis. Although all these techniques have their own advantages and disadvantages, one

    of the most important feature of DEA is the ability to compare many parameters

    simultaneously and come up with a scalar measure of overall performance. DEA provides

    the relative efficiency of each of the firms (which usually are called Decision Making

    Units (DMUs)) in a given set of firms. These DMUs are assumed to be in the business of

    producing various outputs by consuming a set of inputs. In general several inputs are

    required to produce one or more outputs for a DMU. However, in DEA only a few inputs

    and outputs are chosen depending on how critical their contribution is to the effective

    performance of the DMU, in order not to dilute the efficiency analysis with too many

    parameters. The selection of inputs and outputs is of paramount importance in any DEA

    calculations as the results of the study can vary with different sets of inputs and outputs.

    They vary from industry to industry, and even within the industry depending on the

    objective of the efficiency analysis being carried out. It always helps to begin with 2-3

    Inputs (outputs) and slowly build up the number noting down the effect of each additional

    input (output) on the efficiency scores.

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    5.2 DEA Analysis of the above data

    For the Data Envelopment Analysis we are defining three input variables and two output

    variables.

    The input variables are operating expenses, total assets and number of employees. The

    output variables are net profits and total sales revenues. The efficiency will be measured

    by DEA technique and the software used is EMS- efficiency measurement system. In

    order to calculate the efficiency we have used the input oriented measure which

    quantifies the input reduction which is necessary to become efficient holding the outputs

    constant.

    The first column is the DMU- Decision Making Units which in our study are the IT

    companies we have chosen on the basis of our judgment. The second column tells the

    efficiency score of the DMUs calculated by the software. Of all the DMUs, HCL tops the

    list and the efficiency score is 184.87 %. Out of 10 DMUs, the 7 of these have the

    efficiency score above 100% which makes the efficient frontier. This means that these 7

    companies are utilizing the input resources to the best of their use and they act as

    benchmark for the inefficient DMUs.

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    Here, it can be seen that all the values above 100 constitute the efficient frontier. The

    inefficient DMUs are Wipro, Cognizant and Oracle. These have values 93.38, 81.61 and

    88.22 respectively. All the inefficient DMUs are, without any second thoughts, lacking

    somewhere in utilizing their resources efficiently as compared to all other DMUs that are

    lying on efficient frontier. Wipro, Cognizant and Oracle, all these must follow the

    efficient ones.

    The Benchmarks column shows which efficient DMUs must be followed by the

    inefficient DMUs. In the row for the Wipro, the value for the benchmarks is 1(0.14)

    2(0.10) 5(0.88) 7(0.04). This means that TCS, Infosys, HCL and Cisco act as benchmarks

    for the Wipro. Similarly, for Cognizant, TCS, Infosys, HCL and Cisco are the

    benchmarks. Infosys, Wipro and SAP India act as benchmarks for the Oracle.

    Out of all the input and output variables taken into account, the columns following the

    benchmark column describes what variables are important for the companies and must be

    improved upon by the inefficient DMUs, so that they lie on the efficient frontier. For

    Wipro, the value in the column of net profits is 0.03, which means that it must increase its

    net profits by at least 3 percent to qualify for the efficient frontier. But at the same time,

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    for Cognizant Technologies all the values are zero. This means that improvement in none

    of these factors (which have been taken into consideration namely, net profits, total sales

    revenue, operating expenses, number of employees and total assets) can lead the

    cognizant to qualify for the efficient frontier. Whereas, oracle is lacking by a great extent

    because the values for its total sales revenues shows that oracle must increase its sales

    revenues by 86 percent to catch up with Infosys, Cisco and SAP India.

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    Output oriented Measure

    An output oriented measure quantifies the necessary output expansion holding the inputs

    constant. The second column tells the efficiency score of the DMUs calculated by the

    software. But in the output oriented measure the DMU with the lowest efficient score will

    be considered as the most efficient. Of all the DMUs, HCL tops the list and the efficiency

    score is 54.09 %. Out of 10 DMUs, the 7 of these have the efficiency score below 100%

    which makes the efficient frontier. This means that these 7 companies are utilizing the

    input resources to the best of their use and they act as benchmark for the inefficient

    DMUs.

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    Here, it can be seen that all the values below 100 constitute the efficient frontier. The

    inefficient DMUs are Wipro, Cognizant and Oracle. These have values 107.09, 122.54

    and 113.36 respectively. Wipro, Cognizant and Oracle, all these must follow the efficient

    ones.

    The Benchmarks column shows which efficient DMUs must be followed by the

    inefficient DMUs. In the row for the Wipro, the value for the benchmarks is 1(0.15)

    2(0.11) 5(0.94) 7(0.04). This means that TCS, Infosys, HCL and Cisco act as benchmarks

    for the Wipro. Similarly, for Cognizant, TCS, Infosys, HCL and Cisco are the

    benchmarks. Infosys, Wipro and SAP India act as benchmarks for the Oracle.

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    Non Oriented Measure:

    A non-oriented measure quantifies necessary improvements when both inputs and outputs

    can be improved simultaneously. Again in the non-oriented measure, of all the DMUs,

    HCL tops the list and the efficiency score is -29.79 %. Out of 10 DMUs, the 7 of these

    have the efficiency score below 0 which makes the efficient frontier. This means that

    these 7 companies are utilizing the input resources to the best of their use and they act as

    benchmark for the inefficient DMUs.

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    Here, it can be seen that all the values below 0 constitute the efficient frontier. The

    inefficient DMUs are Wipro, Cognizant and Oracle. These have values 3.42, 10.13 and

    6.26 respectively.

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    CHAPTER VII

    RISK FACTORS

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    Risk Factors

    From the study of annual reports we found that the important risk factors to the

    companies are:

    General economic conditions

    Fluctuations in currency exchange rates and related impacts to our operating results

    Natural disasters, like the recent earthquake

    Regulatory changes;

    Political unrest

    Terrorism

    Demand

    Business Disruption

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    Terrorist acts, conflicts or wars (wherever located around the world) may cause damage

    or disruption to any company which adversely affects the employees, partners, suppliers,

    distributors, resellers or customers of the company, to manage logistics, operate

    transportation and communication systems and other critical business operations. The

    potential for future attacks, the national and international responses to attacks or

    perceived threats to national security, have created many economic and political

    uncertainties. Terrorist acts, conflicts, wars may seriously harm any business revenue,

    costs and expenses and financial condition and stock price of it.

    Macroeconomic developments like the recent recessions in the U.S. and Europe and the

    debt crisis in certain countries in the European Union could negatively affect business,

    operating results or financial condition which, in turn, could adversely affect on stock

    price, net profit and revenue.The fluctuation in the Indian economy could cause current

    or potential customers to reduce their information technology (IT) budgets or to be

    unable to fund software, hardware systems or services purchases, which could cause

    customers to delay, decrease or cancel purchases of the products and services and can

    cause customers not able to pay for the product and services and results in delay of

    payments for previously purchased products and services.

    In addition, political unrest in regions, terrorist attacks, and natural disasters, including

    the earthquake and resulting tsunami in Japan, continue to contribute to a climate of

    economic and political uncertainty that could adversely affect the results of operations

    and financial condition, including the revenue growth and net profitability. These factors

    generally have the strongest effect on the sales of new software licenses, hardware

    systems products, hardware systems support and related services and, to a lesser extent,

    also may affect on the renewal rates for software license updates and product support.

    The exchange rate between the Indian rupee and the British pound and the rupee and theU.S. dollar has fluctuated widely in last year and may continue to fluctuate significantly

    in the future. The average value of the rupee as on March 31, 2011 against the British

    pound appreciated by approx 7% and against U.S. dollar by approx 4% over March 31,

    2010. Accordingly, operating results have been and will continue to be impacted by

    fluctuations in the exchange rate between the Indian rupee and the British pound and the

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    Indian rupee and the U.S. dollar, as well as exchange rates with other foreign currencies.

    Any strengthening of the Indian rupee against the British pound, the U.S. dollar or other

    foreign currencies, as witnessed in the last year, could adversely affect the profitability of

    the company.

    Where

    1- Risk of the change in the Economic conditions of the country among the companies

    2-Risk of the change in the Political condition among the companies

    3- Risk of Demand

    4- Risk of Exchange rate fluctuation

    5-Risk of Business Disruption among the companies

    6- Risk of the Natural Disasters

    7- Risk of Terrorism among the companies

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    This figure demonstrates that risks to the IT industry. This paper will focus the attention

    only to 10 IT companies, and from the figure it is clear that the major risk felt by the

    companies is about the change in the economic condition of the country and the exchange

    rate fluctuations. Whereas the risk of the forecasted demand is taken into consideration

    only by 70 percent of the companies and taking into consideration the recent tsunami in

    Japan, 50 percent of companies do feel that they face risk from the natural disasters

    which are not in their control and can happen anytime

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    CHAPTER-VIII

    RESULT

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    RESULT

    The main objective of this study is to calculate the efficiency of Indian IT industry, for

    which we considered the 10 IT companies in India which are major drivers of the growth.

    DEA analysis showed that HCL is the leader in IT industry followed by Cisco, TCS and

    then Infosys technologies. Out of all the DMUs taken into the sample, 7 DMUs were

    performing efficiently and 3 DMUs namely Oracle, Cognizant Technologies and Wipro

    are lacking in the race of becoming the benchmark in IT industry. In spite of the fact that

    Oracle is reporting a net growth of 32 percent in its total revenues, it is standing on the

    position 9 in the table of efficient IT companies. This might be primarily due to reason

    that it has not been focusing on its operating expenses as compared to other efficient

    DMUs. Or it might be the case that the efficient DMUs who tops the list, are utilizing

    their total assets as well as total number of employees really well. The topmost position is

    held by HCL which clearly shows that it has been able to utilize its total assets well or it

    may be incurring fewer expenses on its operations to achieve better revenues.

    Another objective was to find the environmental factors which are not in the control and

    having the effect on the functioning of the IT industry. Secondary study of the data

    showed that almost all the companies consider effect of the economic conditions of the

    country and foreign exchange rate fluctuations most important among other risks faced

    by the industry. The depreciating Indian rupees, recession in Europe, dampening growth

    of Indian industry are the reasons behind this behavior of the companies. Another risk

    which is common to all the companies is the risk of demand. Demand is something which

    cannot be forecasted correctly, and inaccurate forecast of the demand makes companies

    feel that it is risk to the company.

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    CHAPTER- IX

    CONCLUSION

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    CONCLUSION

    From the above discussion of the results showed by data envelopment analysis, we

    conclude that majority of IT companies in India are operating efficiently. Exception to

    this are the 3 big companies namely oracle, cognizant technologies and Wipro. HCL tops

    the list of the most efficient IT companies in India. The major environmental factors

    which can adversely affect the financials of the company and its operations are the

    economy conditions of the country and the exchange rate fluctuations. Almost all the

    companies feel that these two factors pose a great risk to them. After this study a direct

    implication can be for the investors who can invest into the most efficient IT companies.

    In the most efficient IT sector an investor should consider to invest into HCL, TCS,

    Infosys and other such companies lying on efficient frontier. These companies are

    operating efficiently as compared to other peer companies and therefore will give more

    return with less risk.

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    CHAPTER-X

    RECOMMENDATIONS

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    Recommendations

    In this study, we found out that oracle, Cognizant technologies and Wipro are lacking

    behind in the race of the most efficient company in the IT industry. DEA showed that

    oracle needs to improve its total revenues to become the efficient player in the market

    whereas Wipro can lie on the efficient frontier if it is able to increase its net profits by 0.3

    percent. The major environmental risks posed by these companies are economic

    conditions of the country and exchange rate fluctuations. Out of these two factors,

    exchange rate risks can be controlled to some extent by these companies by using optimal

    hedging tools to reduce the risk like forward market hedge, money market hedge and

    options market hedge.

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    CHAPTER- XI

    REFERENCES

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    References:

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    in Banking Institutions: Evidence from the UAE Commercial Banks.

    Beccal Elena, Casu Barbara and Girardone Claudia, (2003) Efficiency and

    Stock Performance in European Banking, Working Paper Series,

    (Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=391668)

    Babalos Vassilios, Caporale Maria Guglielmo, Philippas Nikolas (2009)

    Evaluating Greek Equity Funds Using Data Envelopment Analysis.

    Bosetti Valentina, Cassinelli Mariaester and Lanza Alessandro (2004) Using

    Data Envelopment Analysis to Evaluate Environmentally Conscious Tourism

    Management.

    (Available at: http://www.feem.it/Feem/Pub/Publications/WPapers/default.htm)

    Bukri and Niazi (2003) the privatization effect on Pakistans banking cost and

    allocatinve, technical, scale and pure technical efficiency, European Journal of

    Social Sciences Volume 17, Number 1 (2010)

    (Available at www.eurojournals.com/ejss_17_1_02.pdf)

    Dash Mihir, Charles Christable (2009), A study of technical efficiency of

    banks in India

    FAllon Cayon Edgardo and Sabogal Sarmeinto Julio (2009) Measuring

    relative efficiencies in the Shoe Industry sector in Colombia: A DEA approach.

    Feroz EH, Kim S, Raab RL (2003), Financial Statement Analysis: a data

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    53.

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    Feroz H Ehsan, Goel Sanjay and Raab L Raymond(2006), Performance

    Measurement For Accountability in Corporate Governance: A Data Envelopment

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    (Available at: http://ssrn.com/abstract=1211902)

    Funari Stefania, Basso Antonella (2002), Measuring the performance of ethical

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    Galagedera U.A. Don, Edirisuriya Piyadasa (2004), Performance of Indian

    commercial banks (1995-2002): an application of data envelopment analysis and

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    Grigorian A David, Manole Vlad (2002), Determinants of Commercial Bank

    Performance in Transition: An Application of Data Envelopment Analysis.

    World Bank Policy Research Working Paper 2850

    Kuosmanen Timo and Kortelainen Mika(2006) Valuing Environmental Factors

    in Cost-Benefit Analysis Using Data Envelopment Analysis.

    (Available at: http://www.feem.it/Feem/Pub/Publications/WPapers/default.htm)

    Pasiouras FotiosSifodaskalaki, Emmanouil s and Zopounidis

    Constantin(2007) Estimating and analysing the cost efficiency of Greek

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    Rajput Namita and Gupta Monika, (2011) Impact Of IT On Indian Commercial

    Banking Industry: DEA Analysis, Global Journal of Enterprise Information

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    (Available at www.ejournal.co.in/gjeis/Index.php/GJEIS/article/view/139/74)

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    Tripathy Ishita, Yadav Surendra and Sharma Seema, (2009) Measuring the

    Efficiency of Pharmaceutical Firms in India: An Application of Data

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    (Available at gcoe.ier.hit-u.ac.jp/CAED/papers/id06 2_Tripathy_Yadav_Sharma.pdf)

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    CHAPTER XII

    APPENDICES

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    Appendix 1:

    Table is drawn for the companies with revenue for FY 2011 more than 4000 crores INR

    and double digit growth. All the companies are not included here as companies are

    selected based upon judgement. Ranking has been done based upon revenue in FY 2011.

    Company Name and

    Rank

    Revenue FY 2010

    (INR in crores)

    Revenue FY 2011

    (INR in crores)

    Revenue Growth

    (% per annum)

    1. Tata Consultancy

    services (TCS)

    Rs. 30028 Rs.37325 24.3%

    2. Infosys

    technologies

    Rs. 22742 Rs. 27501 20.92%

    3. Wipro Rs. 27157 Rs. 31098 14.5%

    4.Cognizant

    technology solutions

    Rs. 22961 Rs. 30605 30.93%

    5. HCL technologies Rs. 12290 Rs. 16030 30.43%

    6. Redington India Rs. 13770 Rs. 17478 26.93%

    7. Cisco Systems

    India

    Rs. 160066 Rs. 216090 35%

    8. Oracle India Rs. 13400 Rs. 178110 32.97%

    9. SAP India Rs. 83508 Rs. 95361 14.19%

    10. Tech Mahindra Rs. 4574 Rs. 5140 11.06%

    Source: annual reports of respective companies

    The top 20 companies have contributed over 64 per cent to the combined revenue,

    according to a study by CyberMedia's Dataquest Research. So this sample represents

    considerable market share.

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    Appendix 2:

    Descriptive Input/output parameters table:

    Company Name X1(Rs. In

    Crores)

    X2 (Rs. In

    Crores)

    X3 Y1 (Rs. In

    Crores)

    Y2 (Rs. In

    Crores)

    1. TCS26,146 14,276 184,603 37,325 9,068

    2. Infosys

    technologies 11,599 24,677 130,820 27,501 6,835

    3. Wipro 21,198 26,065 122,385 31,098 5,292

    4. Cognizant tech

    solution 24,923 27,539 137,700 30,605 4,418

    5. HCL

    technologies 13,181 2,238 81,188 16,030 1,646

    6. Redington India 17,030 3,120 78,000 17,478 226

    7. Cisco Systems

    India 80,150 435,475 71,825 216,090 45,165

    8. Oracle India 117,945 367,675 108,000 178,110 42,735

    9. SAP 62,658 155,607 54,589 95,361 23,041

    10.Tech Mahindra 2,092 6,044 38,333 5,140 644

    Xi = Input variablesX3 = No. of employees: proxy for skilled manpower, manpower expenses and

    investment in human resource

    X2 = Total Assets: A direct input to produce output, proxy for size of firm.

    X1= Total operating expenses: cost analysis for producing outcome

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    Yi= output variable

    Y1 = Total Sales revenue

    Y2= Net profit

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    Appendix 3:

    Financial statements

    TCS

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    Tech mahindra:

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    Wipro

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    Redigton India

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    HCL

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