Ijbea vol1 print by iasir journals

ISSN (ONLINE): 2279-0039 ISSN (PRINT): 2279-0020

Issue 8, Volume 1 & 2 March-May, 2014

International Journal of Engineering, Business and Enterprises Applications

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijebea@gmail.com

PREFACE We are delighted to welcome you to the eighth issue of the International Journal of Engineering, Business and Enterprises Applications (IJEBEA). In recent years, advances in science, engineering, and business processes have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJEBEA is publishing high-quality, peer-reviewed papers covering a number of topics in the areas of business process models, engineering and enterprise applications, knowledge engineering science, modeling and designing, control and deployment techniques, e-Commerce applications, B2B and B2C applications, Protocol management and channel management, Mobility, process, engineering, security and technology management, Semantic Web and interfaces, Enterprise applications for software and web engineering,

open-source

platforms,

Human

resource

management,

Operations

management, Organizational and management issues, Supply chain management, Strategic decision support systems, Cloud computing, Risk management, Information technology, Information retrieval systems, Aspect-oriented programming, e-Libraries and e-Publishing, Data mining and warehousing, Distributed AI systems and architectures, Bioinformatics and scientific computing, Knowledge and information management techniques, and other relevant fields available in the vicinity of engineering, business and enterprise applications. The editorial board of IJEBEA is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including business process models, software and technology deployments, ICT solutions, and other related disciplines of engineering, business and enterprise applications. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to engineering, business and enterprises applications. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJEBEA for entrusting us with the important job. We are thankful to the members of the IJEBEA editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this eighth issue, we received 95 research papers and out of which only 33 research papers are published in two volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the fields of engineering, business and enterprises applications.

This issue of the IJEBEA has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in engineering, business and enterprise applications and may open new area for research and development. We hope you will enjoy this eighth issue of the IJEBEA and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Engineering, Business and Enterprises Applications (IJEBEA), ISSN (Online): 2279-0039, ISSN (Print): 2279-0020 (March-May, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:                                                    

e-Commerce applications using web services B2B and B2C applications Advanced web service technologies including security, process management and QoS Surveillance technologies and security policies Security for protocol management Resource and channel management Mobility management Network Security management Technology management Information security management Semantic web for e-Business and e-Learning e-Learning design and methodologies Instructional design methodologies Content management and development Knowledge and information management techniques Enterprise Applications for software and web engineering Open-source e-Learning platforms Internet payment systems Techniques for B2B e-Commerce e-Business models and architectures Service-oriented e-Commerce Human resource management Business-oriented and consumer-oriented e-Commerce Development of e-Business and applications Supply chain management Strategic decision support systems Enterprise resource planning and e-Business Intranet and extranet business applications Enterprise-wide client-server architectures Information systems analysis and specification Strategic issues in distributed development Semantic web technologies and cloud computing Legal aspects of e-Government Risk management Methods and tools for e-Government e-Democracy and e-Voting Operations management Information technology Information retrieval systems Aspect-oriented programming e-Libraries and e-Publishing Intelligent tutoring systems Digital libraries for e-learning Web-based learning, wikis and blogs Social networks and intelligence Social science simulation Information retrieval systems Wired and wireless data communication networks Data mining and warehousing Distributed AI systems and architectures Bioinformatics and scientific computing Knowledge and information management techniques

TABLE OF CONTENTS (March-May, 2014, Issue 8, Volume 1 & 2) Issue 8 Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 14-207

The effects of Philanthropy Social Responsibility on Brand Personality and Creation Corporate Brand Equity in SMEs (A case study in IRAN) Mohammad Reza Hamidizadeh, Morteza Rezaee

01-05

IJEBEA 14-209

The Link between Institutions and Industrial Development: An Empirical Study of Indian States BIMAL C. ROY, DR. SATYAKI SARKAR, DR. NIKHIL RANJAN MANDAL

06-12

IJEBEA 14-211

Rediscovering, Redefining and Rebuilding Manufacturing in the Education Sector S.K.Suman, R.M.Belokar, Harish Kumar Banga, Pratik Belokar

13-17

IJEBEA 14-212

Analysis and Forecasting of Drought by Developing a Fuzzy-Based Hybrid Index in Iran Parisa Lakhaye Rizi, Reza Moghaddasi, Alireza Eghbali

18-23

IJEBEA 14-215

Influence of tool pin profile on mechanical properties of FSP processed aluminium 6063 Chandan Deep Singh, Ripandeep Singh, Napinder Singh, Jaimal Singh Khamba

24-28

IJEBEA 14-217

Online sales promotions of Grocery and other FMCG products in Chennai Entity Alexander CVJ Victoria and Dr. M. Ganesan

29-36

IJEBEA 14-222

Research Perspective Review on Retinal Blood Vessel Detection Dr Ravi Subban, G. Padma Priya, P.Pasupathi, S.Muthukumar

37-42

IJEBEA 14-225

Parametric Optimization of SAW Welding Parameters using Taguchi L9 Array Mr. Pradeep Deshmukh, Prof. S. N. Teli

43-47

IJEBEA 14-228

Database Designing for the Online Examination Application Using SAP-ABAP Preeti Singh Bhadoria, Kapil Nimker, Sanjay Ojha

48-52

IJEBEA 14-229

Insights into Awareness Level and Investment Behaviour of Salaried Individuals towards Financial Products Puneet Bhushan

53-57

IJEBEA 14-238

Implementing security to OFDM symbols of 802.11n networks SANTOSH, VINOD B DURDI

58-61

IJEBEA 14-240

To study the role of manufacturing competency in the performance of Sonalika tractor manufacturing unit Chandan deep Singh, Palwinder Singh*, Jaimal Singh Khamba

62-66

IJEBEA 14-244

BIM creation using point clouds Desislava Georgieva Tanusheva

67-72

IJEBEA 14-251

Employer Branding: The New Mantra for Talent Acquisition Mrs. Shipra Sharma, Ms. Sakshi Chabbra

73-80

IJEBEA 14-252

Analysis of Risk Management of Vendor in Banking Dr Hariharan.N.P, Reeshma.K.J

81-84

IJEBEA 14-253

Competitive Intelligence Loop Contexts in Insurance Industry Dr. Mohammad Reza Hamidizadeh, Dr. Ahmad Roosta, Dr. Jalil Lajevardi, Moghadaseh Mohamadian

85-89

IJEBEA 14-256

Talent Management in TCS Dr. Namita Rath, Ms. Sujata Rath

90-93

IJEBEA 14-260

Cost Optimization and Resource Utilization via Virtualization Using Virtual Box Sonia Bansal, Gurmeet Singh

94-96

Issue 8 Volume 2 Paper Code

Paper Title

Page No.

IJEBEA 14-261

A Study on Stress Related Problems among the Employees Dr.A.Sathish Babu, SK.Irshad

97-100

IJEBEA 14-262

Performance of Reinforced Concrete Beam under Line Impact Loading I.K .Khan

101-104

IJEBEA

Integration of Big Data in Banking Sector to Speed up the Analytical Process

105-110

14-267

Prof. Dr. P.K. Srimani, Prof. Rajasekharaiah K.M.

IJEBEA 14-270

Online Examination Application using SAP ABAP Kapil, Shwetank Sharma, Sanjay Ojha

111-118

IJEBEA 14-271

An Empirical Study of Extracting information for Business Intelligence V.Jayaraj, V.Mahalakshmi

119-121

IJEBEA 14-272

Hierarchical Decentralized Averaging for Wireless Packet Network A R ASWATHA, RAHUL R, M PUTTARAJU

122-126

IJEBEA 14-275

Improvement of Quality through Six Sigma A Case Study R.M.Belokar, Harish Kumar Banga, Jagbir Singh, Pratik Belokar

127-131

IJEBEA 14-276

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The effects of Philanthropy Social Responsibility on Brand Personality and Creation Corporate Brand Equity in SMEs (A Case Study in IRAN) Mohammad Reza Hamidizadeh1, Morteza Rezaee2, Morteza Maddah 1 Professor, Shahid Beheshti University, Tehran, Iran 2 Master of Business Administration, Shahid Beheshti University, Tehran, Iran 3 Master of Executive Management, Yazd University Abstract: The Purpose of this study is to arising understanding and the role of Corporate Social¬ Responsibility in branding and improve corporate personality and creation corporate brand equity in SMEs. This paper category as an applied research considering its aim and it is descriptive and correlation survey research considering its data collection based on field study in Tehran SMEs industrials using a sample with 92 size, this study examines the effects and relationships between latent variables by six hypothesis. The results show that brand equity and personality are predicted by Philanthropy corporate Social Responsibility of SMEs. Of course, total effect of the philanthropy corporate social responsibility is more than other variables on corporate brand equity. Keywords: Philanthropy Corporate Social Responsibility (PCSR); Brand Personality (BP); Corporate Reputation (CR); Corporate Brand Equity (CBE); SME I. Introduction In the early second decade of the 21st century, many of the well-known companies of the world have been damaged by the economic and financial crisis. The consequences of this crisis have increased distrust among the customers of companies. This situation has obligated reviewing the marketing policies of businesses. Kotler et al. [18] in their theory titled "Marketing 3", believed that the marketers should consider the people as complete human with mind, heart and soul instead of seeing them only as consumers; therefore, the customers are not only looking for satisfying their functional and emotional needs by choosing the products and the services, but also they are looking for satisfying their spirit needs (values). Thus, during the economic crisis, considering the human spirit or the values of stakeholders have found more connections with the consumers’ life and the companies have been able to distinguish themselves by their values [18]. One of the main values, which have attracted the attention of the consumers and other stakeholders, is "Corporate Social Responsibility" which on the consumers’ viewpoint is one of the most important ethical values in the market. Because, it has two consequences: The internal consequences (Awareness, Attachment and Attitudes) and other, the external consequences (Purchase, word of mouth and loyalty) ([5], [9]). Hence, the relation between CSR and a brand may be considered as foundation for developing the characteristic and values of the brand and these values are created by differentiation between the market and the firm levels on the product [23]. Now, two key problems of small businesses are lack of real understanding of small corporate social responsibility on the competitiveness [30] and, branding [23] emphasizing on brand alignment with the desires and values of stakeholders in society. As per the evolution of social responsibility, there are three approaches toward social responsibility: "Classical approach, the approach of accountability, and general approach". The question is that: are the small businesses able to achieve equity in branding by social responsibility within the framework of moral values with the general approach? The other issue is the role and importance of ethicoriented social responsibility during crisis with general approach toward achieving personality of company/brand and appropriate corporate reputation along with customers need and values. Hence, to fill this gap, this study intends to study social responsibility reinforcement strategy as an important and effective value for improving perception and behavior of buyers of industrial products. Therefore, this study aims to answer the question that whether PCSR (ethic-oriented) is able to create brand equity for small businesses by mediating corporate personality and reputation; another question is that "Does the social responsibility (ethic-oriented) result directly in creation of corporation brand equity?" And finally, the study discusses the relationship between education levels and PCSR in the corporation. II. Literature review st

Corporate social responsibility- 21 century should be known as the century of social issues considerations and answers. Hence, improving CSR, potential advantages will be created, including optimization of

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communications and relations with consumers and other stakeholders; positive evaluation of product and brand and finally, selection and recommendation of goods exchanges [23]. Because, on one hand, selecting and deciding on a brand at the market is a mental process [33]; and on the other hand, CSR and Sustainable development are two aligned and parallel concepts in the corporation [14]. Carroll in his study titled "social responsibility pyramid of firms", has been divided the social responsibility into four components, including economic needs, observance of general rules and regulations, observance of business ethics and Philanthropy responsibilities [6]. Considering the Carroll’s model, this research defines social responsibility as "Philanthropy social responsibility"; and also, he has interpreted the Philanthropy social responsibility in their studies as "good citizen"; i.e. the firm's participation in various activities that decreases social problems and improves quality of peoples’ life in the society. Brand personality- Today, Brand personality is one of the most fascinating and exciting concepts in marketing; because it has a very important role in creating a strong corporate brand [27]. The review of the related literature shows that "Martineau" [25] is one of the first researchers who discusses the concept of brand personality, but most researchers believe that the definition of Aaker ([2],[13]) from the brand personality are the most comprehensive and prestigious: "A set of human characteristics associated with a brand" [24]. In addition, Abratt and Kleyn [3] state that corporate brand personality can be defined in terms of the human characteristics or traits of employees of the corporation as a whole and will reflect the values, words and actions of all employees of the organization Reputation- Company reputation is an important factor in businesses success [28], because it shows that how much positive and acceptable experiences the stakeholders have on the previous performance and behavior of the organization, i.e. "organization's ability to meet the expectation of all its customers" ([28], [29]). Corporate brand equity- Many researchers attribute to Keller [12] and Aaker [1] the main activities to develop and build the brand equity concepts and scales [17]. Aaker [1] defines brand equity concept as "a set of brand assets (or liabilities) linked to a brand’s name and symbol that add to or subtract from the value provided by a product or service to a firm and/or that firm’s customers". Aaker believes brand equity includes: brand loyalty, brand awareness, perceived quality, brand association and other brand-related assets. Besides, Keller [16] discusses the brand equity from the corporate point of view and based on customer (CBBE) and defines it as: "the differential effect of brand knowledge on consumer response to the marketing of the brand". Table1 explains briefly researches about relationships between these variables. Table1- Literature Review Authors

Relationship

Results

Lee and kang [21]

The impact of brand transgressions (immoral behaviors) on consumer-brand relationship with mediation of brand personality and consumer loss

The negative perception of the customers towards the brand or lack of attention to customers’ moral values has negative effect on brand personality.

Worcester [34], Hsu [15] and Lai et al. [20]

Social responsibility and reputation

They have positive Relationships.

Hsu [15] and Lai et al. [20]

Social responsibility and corporate brand equity

They have positive Relationships.

Brand personality and corporate brand equity

They have positive Relationships.

Brand personality and corporate reputation Corporate reputation and brand equity

They have positive Relationships. They have positive Relationships.

McCorkindale [26], Lin [22] and Valette-Florence et al. [32] Worcester [34] Lai et al. [20]

III. Methodology Hypotheses- This study has six Hypotheses. They are: H1: The perception of buyers about the activities related to social responsibilities of providers has positive effect in providers’ brand personality. H2: The perception of buyers about the activities related to social responsibilities of providers has positive effect in providers’ corporate reputation. H3: The perception of buyers about the activities related to social responsibilities of providers has positive effect in providers’ brand equity. H4: The providers’ Brand personality has positive effect in providers’ brand equity. H5: The providers’ Brand personality has positive effect in providers’ corporate reputation. H6: The providers’ corporate reputation has positive effect in providers’ brand equity.

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Also, Research population includes all purchase managers of small and medium companies in the foodstuff sectors in the industrial town of Tehran in 2012 having less than 50 employees who play a major role in the buying process. The Sampling is done stochastically; and data of this research is collected by e-mails and by preparing a standard questionnaire. E-mails are sent to 130 industrial companies acting in the field of foodstuff in industrial towns of Tehran and during four weeks, complete information was collected from 92 companies (%70.08). In order to evaluate the reliability of latent variables, the indexes of Cronbach's alpha more than 0.7 [7], composite reliability (CR) more than 0.6 [4] and Average Variance Extracted (AVE) more than 0.5 [10] are used. Of course, all of variables have appropriate reliability except for brand personality (α=0.52). In order to achieve good reliability in the brand personality, due to its load factor, two variables of "Aggressiveness and Simplicity" are removed; since, their factor loading are less than "absolute 0.5". Thus, the reliability of the final table will be as follows (Table 2). Table 2-Measurement Model and Reliabilities results Latent variables

Cranach's Alpha

AVE

Reference

PCSR1 PCSR2 PCSR3 PCSR4 CR1 CR2 CR3 CR4 CBE1 CBE2 CBE3 RES

Factor Loading 0.736 0.834 0.835 0.891 0.852 0.839 0.879 0.918 0.831 0.855 0.759 0.911

0.843

0.682

0.895

Singh et al. [11]; and Salmones et al. [31]

0.895

0.762

0.927

Kuenzel et al. [19]; and Lai et al. [20]

0.752

0.666

0.856

Davis et al. [8]

ACT

0.899

0.823

0.739

0.895

Geuens et al. [12]

EMO

0.762

Numbers

Philanthropy Corporate Social Responsibility

Corporation Reputation

Corporate Brand Equity

Brand Personality

IV. Statistical Analysis of Data Demographic sample analysis - According to descriptive analysis, all respondents (92 persons) are men, 26 persons (%28) of them are under 30 years old, 34 persons (%37) are between 30 and 40 years old, 21 persons (%23) between 40 and 50 years old and 11 persons (%12) upper than 50 years old. 11 persons (%12) hold high school Diploma and associate degree, 77 persons (%84) hold bachelor’s degree and 4 persons (%4) holds postgraduate degree. 7 companies have less than 15 personnel, 68 companies have between 15 and 30 personnel and 17 companies have between 30 and 50 personnel. Hypotheses test- The study uses Smart pls2 software for testing the hypotheses. Because, major advantages of the software PLS is the ability to analyse data with small size of the sample. The results are presented in Table3. "T-Value" of all paths is upper than the standard rate of absolute 1.96 and it shows significant and positive relationships between variables. Table 3- Hypotheses Test results Path

Social Responsibility  Corporation Reputation

0.505

T-value

Hypothesis

4.836

supported

2.721

unsupported

13.38

supported

3.756

supported

2.559

supported

2.038

supported

0.584

Brand Personality  Corporation Reputation

0.316

Social Responsibility  Brand Personality

0.719

Social Responsibility  Brand Equity

0.454

Corporation Reputation  Brand Equity

0.263

Brand Personality  Brand Equity

0.203

0.697

0.517

As Shown in Table4, Philanthropy corporate Social Responsibility has the greatest total effect (0.792) on corporate brand equity among the variables; this effect is both direct and indirect. The second effective variable is brand personality (0.286) and finally, the third effective variable is corporate reputation which has only direct effect (0.263) on corporate brand equity.

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Table 4- Total effects of PCSR on OBE Dependent variables Corporate Brand Equity (CBE) Brand Personality (BP) Corporate reputation (CR)

PCSR

0.454

BP CR

0.203 0.263

Effect Indirect (0.505×0.263) + (0.719×0.316×0.263) + (0.719×0.203) = 0.338 (0.316×0.263) = 0.083 --

PCSR

0.719

PCSR

0.505

(0.719×0.316) = 0.227

0.732

0.316

Independent variables

Direct

Total 0.792 0.286 0.263

V. Conclusions and Recommendation - Philanthropy corporate Social Responsibility has the greatest total direct and indirect effects on corporate brand equity based on research model. - The second effective variable on corporate brand equity based on research model is brand personality. - The third direct effective variable on corporate brand equity based on research model is corporate reputation. - There are positive correlations between social responsibility, brand personality and corporate reputation with corporate brand equity; brand personality with corporate reputation; and Philanthropy corporate Social Responsibility with brand personality and corporate reputation. - There is the highest correlation belongs to PCSR and corporate brand equity. - There is the lowest correlation belongs to corporate reputation and brand personality. - Results of correlation test confirm the results of partial least squared (PLS) test. - Results of correlation test confirm the hypotheses and show the positive and significant relation between research variables. - Results of hypotheses’ test show that all hypotheses are acceptable based on paths analysis and it shows significant relationships between variables. The result of first research hypothesis emphasis the results of Lee and kang findings [21]. The result of second research hypothesis emphasis the results of Worcester findings [34], Hsu [15] and Lai et al [20]. The result of third research hypothesis emphasis the results of Hsu [15] and Lai et al [20] findings. The result of fourth research hypothesis emphasis the results of McCorkindale [26], Lin [22] and Valette-Florence, Guizan and Merunka [32] findings. The result of fifth research hypothesis emphasis the results of Worcester findings [34]. The result of sixth research hypothesis emphasis the results of Lai et al findings [20]. - Results show that appropriate effect of level of education on social responsibility. - Results show that CSR increases by choosing purchase managers with higher level of education and corporate can benefit more equity and personality.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

D. Aaker, Building strong brands. Bath, Great Britain: The Bath Press, 1996. J. L. Aaker, “Dimensions of brand personality,” Journal of Marketing Research, Vol. 34, 1997, pp. 347-356. R. Abratt and N. Kleyn, “Corporate Identity, Corporate Branding and Corporate Reputations: Reconciliation and Integration,” European Journal of Marketing, Vol. 46 No. 7, 2012, pp. 10–10. R. P. Bagozzi and Y. Yi, “On the evaluation of structural equation models,” Journal of the Academy of Marketing Science, Vol. 16, 1988, pp. 74-94. C. B. Bhattacharya and S. Sen, “Consumer-company identification: a framework for understanding consumers’ relationships with companies,” Journal of Marketing, Vol. 67, 2003, pp. 68-76. A. B. Carroll, “The Pyramid of Corporate social Responsibility: Toward the Moral Management of Organizational Stakeholders,” Business Horizons, 1991, pp. 4-8. L. J. Cronbach, “Coefficient Alpha and the Internal Structure of Tests,” Psychometrical, Vol. 16, 1951, pp. 297- 334. D. F. Davis, S. L. Golicic and A. J., “Marquardt, Branding a B2B Service: Does a Brand Differentiate a Logistics Service Provider?,” Industrial Marketing Management, Vol. 37 No. 2, 2008, pp. 218–227. P. Ellen, D. J. Webb and L. A. Mohr, “Building corporate associations: Consumer attributions for corporate socially responsible programs,” Academy of Marketing Science, Vol. 34 No. 2, 2006, pp. 147-157. C. Fornell and D. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, Vol. 18, 1981, pp. 39-50. M. M. García de los Salmones and I. Rodríguez del Bosque, “Corporate social responsibility and loyalty in services sector,” EsicMarket, Vol. 138, 2011, pp. 199-221. M. Geuens, B. Weijters and K. D. Wulf, “A new measure of brand personalitym,” Intern. J. of Research in Marketing, Vol. 26, 2009, pp. 97–107. M. R. Hamidizadeh, M. R. karimi Alavije and M. Rezaee, “The examination of relationships between personality dimensions & Brand equity and moderator role of Ethical Attributes,” New Marketing Research, Vol. 2 pp. 3, 2012, pp. 35-50

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[14] D. Hildebrand, S. Sen and C. B. Bhattacharya, “Corporate Social Responsibility: A Corporate Marketing Perspective,” European Journal of Marketing, Vol. 45 No. 9, 2011, pp. 1353-1364. [15] K. T. Hsu, “The Advertising Effects of Corporate Social Responsibility on Corporate Reputation and Brand Equity: Evidence from the Life Insurance Industry in Taiwan,” Journal of Business Ethics, Vol. 109 No. 2, 2011, pp. 189-201. [16] M. Juntunen, J. Juntunen and J. Juga, “Corporate Brand Equity and Loyalty in B2B Markets: A Study among Logistics Service Purchasers,” Journal of Brand Management, Vol. 18, 2011, pp. 300–311. [17] K. L. Keller, “Conceptualizing, Measuring and Managing Customer-Based Brand Equity,” Journal of Marketing, Vol. 57 No. 1, 1993, pp. 1-22. [18] P. Kotler, I. Setiawan and H. Kartajaya, (2010), Marketing 3.0: From Products to Customers to the Human Spirit, Danesh Ofogh Publication, translated by: Ebrahimi, A., AAli, S., zende, A.B. and Ashrafi, E.A., Industrial management organization in Tabriz city of Iran (2011), pp. 22-24. [19] S. Kuenzel and S. V. Halliday, “The Chain of Effects from Reputation and Brand Personality Congruence to Brand Loyalty: The Role of Brand Identification,” Journal of Targeting, Measurement and Analysis for Marketing, Vol. 18, 2010, pp. 167-176. [20] C. S. Lai, C. J. Chiu, C. F. Yang and D. C. Pai, “The Effects of Corporate Social Responsibility on Brand Performance: The Mediating Effect of Industrial Brand Equity and Corporate Reputation,” Journal of Business Ethics, Vol. 95, 2010, pp. 457–469. [21] H. Lee and M. S. kang, “The Impact of Brand Transgressions on Relationship Strength: Moderating Roles of Brand Personality and Consumer Loss Type,” Proceedings of the Academy of Marketing Studies, Vol. 15, No. 1, 2010, pp. 29-35. [22] L.Y. Lin, “The Relationship of Consumer Personality Trait, Brand Personality and Brand Loyalty: An Empirical Study of Toys and Video Games Buyers,” Journal of Product & Brand Management, Vol. 19 No. 1, 2010, pp. 4–17. [23] A. Lindgreen, Y. Xu, F. Maon and J. Wilcock, “Corporate Social Responsibility Brand Leadership: A Multiple Case Study,” European Journal of Marketing, Vol. 46 No. 7, 2012,pp. 6–6. [24] N. Maehle and R. Shneor, “On congruence between brand and human personalities,” Journal of Product & Brand Management, vol. 19 No. 1, 2010, pp. 44–53. [25] P. Martineau, Motivation in Advertising, McGraw-Hill, New York, NY, 1957. [26] T. McCorkindale, “Does familiarity breed contempt? Analyses of the Relationship among Company Familiarity, Company Reputation, Company Citizenship, and Company Personality on Corporate Equity,” Public Relations Review, Vol. 34, 2008, pp. 392–395. [27] T. C. Melewar, M. Gotsi and C. Andriopoulos, “Shaping the Research Agenda for Corporate Branding: Avenues for Future Research,” European Journal of Marketing, Vol. 46 No. 5, 2012, pp. 1–1. [28] K. Money, S. Rose and C. Hillenbrand, “The impact of the corporate identity mix on corporate reputation,” Brand Management, Vol. 18 No. 3, 2010, pp. 197–211. [29] M. Omar, R. L. Williams Jr and D. Lingelbach, "Global Brand Market-Entry Strategy to Manage Corporate Reputation,” Journal of Product & Brand Management, Vol. 18 No. 3, 2009, pp. 177–187. [30] M. E. Porter and M. R. Kramer, “Strategy and Society: The Link between Competitive Advantage and Corporate Social Responsibility,” Harvard Business Review, Vol. 84 No. 12, 2006, pp. 78–92. [31] J. Singh, M. M. Garcia de los Salmones Sanchez and I. Rodríguez del Bosque, “Understanding Corporate Social Responsibility and Product Perceptions in Consumer Markets: A Cross-cultural Evaluation,” Journal of Business Ethics, Vol. 80, 2008. pp. 597–611. [32] P. Valette-Florence, H. Guizani and D. Merunka, “The Impact of Brand Personality and Sales Promotions on Brand Equity,” Journal of Business Research, Vol. 64, 2011, pp. 24–28. [33] Y. J. Wang, M. Hernandez, M. Minor and J. Wei, “Superstitious beliefs in consumer evaluation of brand logos: implications for corporate branding strategy,” European Journal of Marketing, Vol. 46 No. 5, 2012, pp. 712-732. [34] S. R. Worcester, ”Reflections on corporate reputations,” Management Decision, Vol. 47 No. 4, 2009, pp. 573-589.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net THE LINK BETWEEN INSTITUTIONS AND INDUSTRIAL DEVELOPMENT: AN EMPIRICAL STUDY OF INDIAN STATES 1

Bimal Chandra Roy, 2 Dr. Satyaki Sarkar, 3 Dr. Nikhil Ranjan Mandal 1 Assistant Professor, 2 Associate Professor, Department of Architecture, Birla Institute of Technology, Mesra, Ranchi – 835215, India. 3 Professor, School of Planning and Architecture, Bhopal, India. ______________________________________________________________________________________ Abstract: Industrial development is closely linked to the existence of good institutions. Though the term ‘institutions’ covers a very wide spectrum, this paper considers the three major components of institutions- legal institution, state intervention as an institution and political institution . For better gauging the industrial development, two measures have been considered- percent growth in State Gross Domestic Product (SGDP) and the level of industrial development. This study empirically examines and suggests that the state intervention as an institution is significant in explaining the variations in SGDP growth whereas all three components of institutions play a highly significant role in explaining variations in the extent of industrial development across the Indian states. Keywords: Institutions, Governance, Rule of Law, Legal Efficiency, Political Stability ________________________________________________________________________________________

INTRODUCTION

Development economics of different countries or different states tries to find out the answer of a basic question why different countries or different states within a country grow differently resulting into different degrees of income inequalities. Ayami (1997) discusses the cross country comparison and finds that governance and institutions which are country-specific factors, play a dominant role in determining the growth of a country. Even countries with similar resource endowments have experienced sharply different economic growth because of country-specific governance and organizations. Examples are North Korea versus South Korea, Kenya versus Tanzania, and India versus Pakistan. A well maintained setup of institutions encourages components of economic development to participate in fair and productive economic activities and discourages rent-seeking and illegal activities in an economy. Poor institutions force the economy to a low-level equilibrium due to the disincentives created by the non productive role of the economic agents (Dash and Raja, 2009). The literature that focuses on the role of a government or state maintains that the interventionist activity of the state influences the economic outcomes to a considerable extent (Buchanan and Tabellini, 2005) .The enforcement of efficient legal institutions, protection of property rights and well-enforced rule of law have been recognized as prerequisite for economic prosperity. The role of a state is gauged by two important performers: the existing quality of governance and the extent of state intervention in economic activities. The quality of governance can be judged by the enforcement of the rule of law, fiscal management, and expenditures on development-related activities (Schaefer and Raja, 2006). It is found that the state acts as a grabbing hand rather than a helping hand; it redistributes and appropriates the wealth instead of generating and protecting it. Thus, due to its self-interested character, if the governments were given policy powers that influenced the market, it would fail to bring about effective economic development (Kaufmann et.al., 2002).The political institutions of a nation determine its economic outcomes indirectly by influencing economic institutions (Acemoglu et.al., 2001). A politically unstable society makes investments risky and uncertain by frequently changing the Government and its decisions. Political instability discourages investments and productive economic activities (Barro, 1991, Alesina et.al., 1996, Brunetti and Weder, 1998, and Svensson, 1998). Empirical findings suggest that the extent of state intervention is significant in explaining the variations in state’s SGDP growth whereas legal institutions and political institutions both play a significant role in explaining variations in the extent of industrialization across the states. The paper is organized as follows: the following section is devoted to a discussion of the literature on the role of institutions in industrial development. The third section describes the objectives of this study. The fourth section contains data and the methodology used. The fifth section contains the empirical analysis and the results. The sixth section contains the discussion of the results and the conclusion.

II. THE LITERATURE REVIEW The significant positive role of the institutions on the economic development have been established by a number of cross-country empirical studies in explaining the disparity in growth rate and standards of quality of life

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across different countries over a time (Aron, 2000; Rodrik et.al., 2004; Hall and Jones, 1999; Clague et.al., 1999; Svensson, 1998; Levine, 1998; La Porta et.al., 1999; Mauro, 1995; Knack and Keefer, 1995 and 1997; Barro, 1991 and 1996; Scully and Slottje, 1991). According to Chong and Calderon (2000), a country’s institutional framework is an important factor for not only its economic performance but also the way, how income is distributed among its members. In many researches, information regarding the quality of institutions is generally taken from the international agencies like International Country Risk Guide (ICRG) and the Business Environment Risk Intelligence (BERI). Most of the abovementioned studies suggest that economic performance can be guaranteed only when a country-specific institution is adopted successfully.

A. Institutions and Industrial Performance in India Poor institutions can restrict the economy from using efficient production techniques, whjch in turn would force the country to remain at the ‘low-equilibrium’ trap with low per capita income for a long time, which is the case in India until recently (Dreze and Sen, 1997). Several interesting questions are raised based on this argument, about India's productive efficiency, technological progress and overall growth process during the pre-reform period of 1991. It may be noticed that the government industrial policy did not produce the expected results of increasing employment and reducing the interregional income disparity, though the industrial output increased as discussed by Rosen (1992). To study the role of institutional qualities on the industrial performance it is necessary to understand the growth path followed by India over a number of years since independence. The basic organisational setup followed in India for its industrialisation process has been the heavy dependence on public sector units and a limited entry for new private sector firms and also to stop expansion of existing firms in the production of low priority areas. Capital goods such as Steel and Cement were given to public sector enterprises, while the other low priority productions such as consumer goods were given to private sector. Public sector industries instead of making profits have in fact added huge losses over the years. As a result, instead of being a source of reinvestible surplus, they have become only a source of liability to the economy. Lack of profitability in the public sector has been partly the result of a low rate of capacity utilization. In conclusion, it may be stated that most of the study of institutions and economic performance across the Indian states has only been done with a view to ranking the Indian states and observing whether this ranking has stayed over time or changed. The use of perceptional indices, however, has many problems associated with it and may not give an accurate picture. The question of whether the perceptional indices correlate with the prevailing ideas on the institution-economic development linkage has not been studied so far. Using fiscal deficit as an indicator of fiscal governance would also show misleading results; hence, under index of fiscal governance, different variables are required to be considered. This paper is an attempt to answer these issues and raise striking questions on the role of governance and the linkages between institutions and economic performance.

III. OBJECTIVE OF THE STUDY Most of the earlier research works, on institutions and industrial development across the Indian states, have been performed either with a view to ranking the states or to observe whether this ranking has changed over time. Though the method using perception indices has many problems associated with it, however whether these indices correlate significantly with the industrial development, has not been studied so far. This study is as an attempt to fill this gap and raise pertinent questions on role of different institutions in industrial development. Consequently the objective of this study is framed to study the role of different components of institutions in industrial development of the Indian states by analysing the significance levels of the co-efficient values, using multiple OLS Regression techniques.

IV. THE DATA AND METHODOLOGY In this paper four most economically developed Indian states have been considered from each of the four zones of India namely West Bengal from East Zone, Maharashtra from West Zone, Punjab from North Zone and Tamilnadu from South Zone. Jharkhand state has been considered as an additional state as it endows almost 37 percent of minerals of India with a considerable industrial setup1. But the level of industrialization has not translated into high levels of income for Jharkhand 2. In this process various proxies are used to find out how significant is the components of the Institutions-i) Legal institutions, ii) State intervention as an institutions and iii) Political Institutions, over three dimensions of economic development namely per capita income, percentage growth in SGDP and level of industrialisation, which are collected from various secondary data sources. To perform this cross-sectional study, data for the time period of seven years (from 2004-05 to 20010-11) have been collected. Seven proxies of institutions are used to capture three dimensions of the institutional aspects in the economic activities. This paper formulates institutional indices based on secondary data for the time period of seven years (from 2004-05 to 2010-11) and uses statistical

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methods to test the hypothesis that institutions affect economic performance. This method however ignores the issues that are associated with obtaining information that is perception-based (Aron, 2000).

The common problem for such type of cross-section analysis is multicollinearity. Principal component analysis (PCA) is used to solve this problem. PCA is applied to the proxies who are having high correlation amongst each other. Since the units of measurement of different correlated variables are different, the rotated correlation matrix is used to get the corresponding weight. After standardizing the data, it is multiplied with the weight as suggested by PCA to arrive at the corresponding Indices or composite indices. Finally, three principal components are retained which have extracted 93.8 percent of variance of the dataset. The obtained weights are multiplied by the corresponding standardized values of the variables to arrive at the indices3. Since the proxy of the Creditors’ Property Rights Protection receives the highest weight in the first principal component, the resulting index is named the Index of Property Rights. The second highest weight in the first component is for Average Disposal rate of cases per court, for which the resulting index is named Index of Legal Efficiency. Similarly, the second principal component suggests for the three indices-named as i) the State as a provider of necessary infrastructure- a combination of two variables -named surfaced road as a proportion of total roads and percentage of households having access to a telephone , ii) Index of Economic Freedom-which is a measure of total Govt. Expenditure as a percentage of SGDP and iii) Index of Fiscal Governance- prepared combining two variables named revenue expenditure as a percentage of total expenditure and interest payment as a percentage of total expenditure. The third principal component, which has the highest weight to Transmission and Distribution (T&D) Loss considered as a proxy for the Index of Rule of Law. The second highest weight in third component is for the number of times the president’s rule imposed which when combined with the next highest value for coalition government generates the Index of Political Stability. The resulting seven indices no longer have the problem of multicollinearity and can be used together in a regression equation. The details of the dependent and independent variables are discussed in the next sections. 1

Source:Jharkhand.gov.in/industry World Bank Report no. 36437-IN; Jharkhand: Addressing the challenges of inclusive developments 3 For details refer the tables in Annexure-I. 2

A. The Dependent Variables For better understanding and proper gauging the industrial development of states, two dimensions of it are considered based upon the following justifications(i) Percentage SGDP growth rate among the states is the first variable considered. This is an overall measure of development of states and is used routinely in many studies. This is a measure of the comparative percentage growth year wise, of the state GDP, in different states, reflecting the economic growth rate. The set of institutions is regressed over this variable and the results are discussed in Model-1. (ii) Index of industrial development is considered as the second variable because the extent of industrialization has significant linkage effects that influence the level of economic development. It is measured as the ratio of the contribution of the secondary sector to total state GDP. The set of institutions is regressed over this variable and the results are discussed in Model-2.

B. Selection of the Proxies and the Formulation of the Indices to be referred for independent variables The components of institutions and the justified selection of the corresponding proxy variables are discussed in Table 1.1. Institutions

Components of the Institutions 1 a) Index of Legal Efficiency 1 b) Index of Property Rights

1.Legal Institutions 1 c) Index of Rule of Law

Table 1.1:Institutions and the Selection of the Proxies Description of the Proxy Variables • Disposal rate of cases per court has been considered as a proxy which covers the efficiency level of the legal institutions. A higher value is an indication that pendency is less with a quicker disposal of cases resulting into prevention of productive activities. It is the measure of the degree of risk that banks face across the states. one of the aspects of property rights which is considered as a proxy for this study is the Credit-deposit ratio of commercial banks. The credit deposit (CD) ratio is a measure of the effectiveness of the credit delivery system. This is a perception institution and most of the cross country studies have used the rule of law indices prepared by international agencies like ICRG, BERI and the World Bank. This study has used Transmission and distribution (T & D) loss as a percentage of total generation as a proxy for rule of law with the following justificationT & D losses occur due to two reasons- (i) loss due to transmission which is a technical phenomenon and (ii) loss due to theft which is mainly due to illegal connection of electricity from the transmission. If it is assumed that the loss due to technical reasons would be uniform throughout the state as the technology of generation and distribution does not vary significantly across the country. If the rule of law will be poorer, the probability of being caught will be very low and people will find

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2 a) Index of Economic Freedom

2. State Intervention as an Institutions

2 b) Index of Fiscal Governance

2 c) Index of State as a provider of Necessary Infrastructure

3.Political Institutions

Index of Political Stability

that power theft is very easy. Hence this can be taken as a proxy for rule of law. This index reflects the extent to which the state is involved in economic activities. The proxy considered for this index is the Ratio of total expenditure to state gross domestic product (SGDP). A high value of the ratio of total expenditure to state gross domestic product (SGDP) shows greater extent of intervention of state in various economic activities which may result into more scope of corruption or rent seeking.

A poor fiscal governance may not attract the the private economic agents for the productive economic activities. Two proxy variables are considered to capture this index(i)Revenue expenditure as a percentage of total expenditure- a high ratio value indicatesthat more resources are utilised for generating revenue which is redistributive for the development of the state.a low value indicates an inefficient utilization of the resources and a poorfiscal management. (ii) Interest payments as a percentage of total expenditure- if a large amount is devoted to the interest payments on debts, then few amount is left for the developmetal activities reflecting a poor fiscal management of the state. The role of infrastructure in industrial development and hence in economic outcomes is well established by various studies. Two variables are considered to capture this(i) Ratio of surfaced (paved) roads to total roads. This indicates the quality of road infrastructure and a higher ratio represents the maintenance of good transport facilities by the state. A developed road infrastructure reduces the total transaction costs of economy by saving time and minimizing transportation costs, which attracts the investment projects. (ii)Percentage of the population accessing telephone connections. An efficient telecommunications system will reduce the costs of communication and will make transactions cheaper and quicker. Two variables are used in order to capture the political scenario of major Indian states. • Number of times the President’s rule was imposed. The imposition of the President’s rule indicates a poor political scenario in a state. President’s rule is generally imposed when none of the political parties gets a majority or if the party in power fails to maintain law and order in the state. If this happens frequently, then a state will fail to attract economic investors and economic outcomes will always be unsatisfactory • Number of times the Chief Ministers headed a coalition form of government. The main problem with a coalition government is that it is not necessarily stable and mere for survival of the coalition government development in all fields are sacrificed by the politicians. Reversal of policies may create an environment which distracts the investment scenario. Hence a high value will suggest high degree of political instability resulting into less development

V. EMPIRICAL ANALYSIS AND THE RESULTS A. Regression Results Multiple ordinary least squares regression analysis is used to analyse the statistical significance level of the different indices formulated for quality of institutions and to explain the variations in the percentage growth in SGDP and level of industrialisation across the states. The results are discussed in Table 1.2.

Dependent Variables Independent Variables Index of Legal Efficiency

Table 1.2: Regression Results MODEL 1 SGDP

Index of Political Stability Index of Rule of Law Index of Property Rights Protection Index of State as a provider of infrastructure services Index of Fiscal Governance Index of Economic Freedom

Intercept

MODEL 2 LID

-0.7319 (-0.5716) -1.997 (-0.678) 8.755 (1.754)* 3.215 (3.311)*** 0.603 (0.485) 178.89 (2.769)** -259.66 (-2.848)***

-1.359 (-2.346)** 4.807 (3.6068)*** 9.836 (4.355)*** 4.486 (10.212)*** 2.775 (4.937)*** 63.683 (2.179)** -97.314 (-2.359)**

7.038*** (8.173)

15.06*** (24.424)

R-Squared 0.5937 0.917 Adjusted R-Squared 0.4264 0.883 F-Statistics 3.549* 26.776*** Degree of Freedom (7,5) (7,5) Abbreviations: SGDP: Percentage Growth in State Gross Domestic Product, LID: Level of Industrial Development Notes: The figures below coefficient measures within parenthesis are the t-statistics values

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*** Significant at 1% significance level ** Significant at 5% significance level * Significant at 10% significance level

The results in Model 1 suggest that it is a weak model as reflected by the comparative low value of adjusted R 2 which explains only 59.4 percent variations. The major finding of this model is the Index of Property Rights which is highly significant and the Index of Fiscal Governance which is significant at 10 percent significance level showing the positive correlation with the degree of state intervention in economic performance. In Model 2, the set of institutions are regressed over level of industrial development and it is found to be satisfying all the criteria of a good fit model with a very high explanatory power covering around 88.3 percent variance in it. The Indices of Political Stability, Rule of Law, Property Rights and State Infrastructure do affect the level of industrial development and hence the overall economic performance of the states, suggested by the positive values of coefficients, is highly significant at 1percent level of significance. In this model Index of Fiscal Governance is also significant at 10 percent level of significance showing that states cannot overlook or ignore this for a better level of industrialisation. Surprisingly the Index of Legal Efficiency and Index of Economic Freedom both have negative signs in Model 2, which is against the expectations but the values are significant at 5 percent level of significance suggesting these indices somehow do affect the level of industrial development in the states considered. The intercept in both models are also highly significant suggesting the initial level of economic growth is positively influenced by both the models considered and hence by all the components of institutions.

VI. DISCUSSION OF THE RESULTS AND CONCLUSION From the results it could be concluded that institutional qualities and the quality of governance do play a role in explaining the economic performance across the states under consideration. The institutions play a significant role in explaining the variations in the level of industrialisation (Model2) as well as in percentage growth in SGDP (Model 1) of the states. However if political stability, rule of law and state as a provider of necessary infrastructure are removed from the analysis then property rights emerge as a significant factor influencing the economic development. This suggests that while institutional qualities play a strong role in economic development but are overshadowed by quality of governance e.g. political stability, rule of law and state as a provider of necessary infrastructure. The measure of quality of governance as the political stability has a very strong effect on the level of industrial development. It is assumed that a state with low political stability where there is a rule of coalition govt, face a lot of difficulties in implementing the policies since the political interests of different political parties don’t match. All the policies and development are sacrificed only for survival of a coalition government, and enjoying their own political benefits and thus the government works for its own interests rather than in the interests of the people at large. However, this is tested by a proxy variable in this analysis and cannot be controlled by any policy. A. The Policy Orientation From a policy perspective it is suggested that the states should spend on developmental expenditures rather than on non-development expenditures. Low quality of institutions would create a lot of obstacles in realizing the true potential of the states and must not be ignored. The development of the states depends heavily on how efficiently resources are used which is further determined by the quality of governance and the prevailing policy environment. Good governance affects the growth and development in manifolds. First, it affects the effectiveness of the public and private sector developmental programmes in the state. Poor administration and mismanagement are now widely accepted as the factors reducing the effectiveness of many government programmes. The general ‘law and order’ –broadly covered by ‘rule of law’ and ‘legal efficiency’ is highly responsible factor of governance which creates an environment conducive to investment. A better rule of law and faster disposal rates by courts and police would certainly have a positive correlation with the development of the states. The result of good governance will facilitate the infrastructure development which itself is the welcoming door for many other developments in various sectors. Another channel through which the growth of the state can be stimulated using the quality of governance at the state level is by making the policy environment more business friendly. A new entrepreneur setting of an industrial unit needs thirty separate permissions from different departments responsible for state level clearances, e.g. those related to environment regulations, utilities, health, sanitary and safety inspection, labour welfare regulation, sales tax, etc. The positive development in recent times is that many states have taken initiatives in this area and have introduced simplified procedures and single-window arrangements to improve the business climate. However these are very recent initiatives and the lead has been taken only by the better performing states. Such type of reforms in the regulatory system is highly needed for the speedy development of the states.

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References: Aron, J. “Growth and institutions: a review of the evidence”, The World Bank Research Observer, vol.15, No. 1, pp. 99-135, 2000. Bardhan, P. Scarcity, Conflicts and Cooperation: Essays in Political and Institutional Economics of Development (Cambridge, MIT Press), 2004. “Institutions matter, but which ones?” Economics of Transition, vol. 13, No. 3, pp. 499-532, 2005. Barro, R.J. “Economic growth in a cross section of countries”, Quarterly Journal of Economics, vol. 106, No. 2, pp. 407-444, 1991. “Determinants of economic growth: a cross-country empirical study”, Working Paper 5698 (Cambridge, National Bureau of Economic Research), 1996. Bhide, S., R. Chadha and K. Kalirajan. “Growth interdependence among Indian states: an exploration”, Asia-Pacific Development Journal, vol. 12, No. 2, pp. 59-80, 2005. Chong Alberto and Cesar Calderon . “Institutional Quality and Income Distribution”, Economic Development and Cultural Change, Vol. 48, pp.761-786, 2000. Clague, C., P. Keefer, S. Knack and M. Olson. “Contract-intensive money: contract enforcement, property rights, and economic performance”, Journal of Economic Growth,vol. 4, No. 2, pp. 185-211,1999. Debroy, B. and L. Bhandari. “India’s best and worst states”, India Today, August 16, 2004. Dreze, J. and A. K. Sen. Indian Development: Selected Regional Perspectives, Delhi: Oxford University Press, 1997. Hall, R.E. and C.I. Jones. “Why do some countries produce so much more output per worker than others?” Quarterly Journal of Economics, vol. 114, No. 1, pp. 83-116, 1999. Indicus Analytics. “Measuring Inter-State Differences in Investment Climate”, report of a project conducted for the Twelfth Finance Commission (New Delhi), 2004. Knack, S. and P. Keefer. “Institutions and economic performance: cross-country tests using alternative institutional measures”, Economics and Politics, vol. 7, No. 3, pp. 207-227, 1995. Keefer, P. and S. Knack. “Why don’t poor countries catch up? A cross-national test of an institutional explanation”, Economic Inquiry, vol. 35, No. 3, pp. 590-602, 1997. La Porta, R., F. Lopez-de-Silanes, A. Shleifer and R. Vishny. “The quality of government”, The Journal of Law, Economics, and Organization, vol. 15, No. 1, pp. 222-279, 1999. Levine, R. “The legal environment, banks, and long-run economic growth”, Journal of Money, Credit and Banking, vol. 30, No. 3, pp. 596613, 1998. Mauro, P. “Corruption and growth”, Quarterly Journal of Economics, vol. 110, No. 3, pp. 681-712, 1995. North, D.C. “Institutions and economic growth: an historical introduction”, World Development, vol. 17, No. 9, pp. 1319-1332, 1989. Institutions, Institutional Change and Economic Performance (Cambridge, Cambridge University Press), 1990. Rao, M.G. “Fiscal Federalism in India: Emerging Challenges”, Silver Jubilee Lecture, unpublished manuscript, Centre for Economic and Social Studies (CESS), Hyderabad, India, 2005. Rodrik, D., A. Subramanian and F. Trebbi. “Institutions rule: the primacy of institutions over geography and integration in economic development”, Journal of Economic Growth, vol. 9, No. 2, pp. 131-165, 2004. Rosen, G. Contrasting Styles of Industrial Reform: China and India in the 1980s, Chicago: Chicago University Press, 1992. Subramanian, A. “The evolution of institutions in India and its relationship with economic growth”, Oxford Review of Economic Policy, vol. 23, No. 2, pp. 196-220, 2007. Svensson, J. “Investment, property rights, and political instability: theory and evidence”, European Economic Review, vol. 42, No. 7, pp. 1317-1341, 1998. Scully, G.W. and D.J. Slottje. “Ranking economic liberty across countries”, Public Choice, vol. 69, No. 2, pp. 121-152, 1991.

ANNEXURE-I Table: 1.3: Correlation Matrix, before applying Principal Component Analysis (PCA) Notes: Figures in parentheses represent probability levels **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). AVG_DISPO PRESI_R COALI_G TDLOSS AVG_DISPO

CD_R

SUR_ROAD TELE_HH REV_EXP INT_PAY TOTAL_EXP

1 -

PRESI_R

.136 (.518)

1 -

COALI_G

-.137 (.514)

.556** (.004)

1 -

TDLOSS

-.187 (.371)

.402* (.046)

.625** (.001)

1 -

CD_R

.586** (.002)

.034 (.872)

-.074 (.723)

-.253 (.223)

1 -

SUR_ROAD

-.885** (.000)

-.218 (.295)

.071 (.736)

.065 (.758)

-.547** (.005)

1 -

TELE_HH

.514* (.010)

-.370 (.075)

-.489* (.015)

-.728** (.000)

.214 (.315)

-.389 (.061)

1 -

REV_EXP

.154 (.462)

.021 (.920)

.116 (.581)

-.028 (.894)

-.161 (.442)

-.146 (.486)

.332 (.113)

1 -

INT_PAY

.074 (.725)

-.050 (.814)

-.083 (.693)

-.183 (.380)

-.172 (.412)

-.091 (.667)

.238 (.264)

.875** (.000)

1 -

TOTAL_EXP

.153 (.466)

.016 (.941)

.100 (.634)

-.041 (.845)

-.166 (.428)

-.147 (.482)

.329 (.116)

.999** (.000)

.895** (.000)

1 -

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Abbreviations; AVG_DISPO, Average Disposal rate of cases per court; PRESI_R, number of times the President’s Rule was imposed ; COALI_G, number of times the CM headed a coalition form of government ; TDLOSS, Transmission & Distribution Loss as a percentage of generation ; CD_R, credit-deposit ratio of commercial banks across the states ; SUR_ROAD, ratio of surfaced roads to total roads ; TELE_HH, percentage of population accessing telephone connections ; REV_EXP, revenue expenditure as a percentage of total expenditure ; INT_PAY, interest payment as a percentage of total expenditure ; TOTAL_EXP, ratio of total expenditure as percentage of total state gross domestic product (SGDP)

ANNEXURE-I- continued Table 1.4: Weights Assigned after applying PCA Rescaled Component 1

.853

.233

.214

PRESI_R

.055

-.238

.513

COALI_G

-.166

-.315

.363

TDLOSS

-.317

-.489

.522

CD_R

.889

-.379

-.245

SUR_ROAD

-.866

-.187

-.460

TELE_HH

.477

.781

-.383

REV_EXP

.037

.553

.049

INT_PAY

-.014

.486

.096

TOTAL_EXP

.034

.557

.059

AVG_DISPO

Statistics Eigenvalues

2.531

1.816

1.522

Percentage of variance extracted 59.4 81.4 93.8 (Cumulative) Abbreviations; AVG_DISPO, Average Disposal rate of cases per court; PRESI_R, number of times the President’s Rule was imposed ; COALI_G, number of times the CM headed a coalition form of government ; TDLOSS, Transmission & Distribution Loss as a percentage of generation ; CD_R, credit-deposit ratio of commercial banks across the states ; SUR_ROAD, ratio of surfaced roads to total roads ; TELE_HH, percentage of population accessing telephone connections ; REV_EXP, revenue expenditure as a percentage of total expenditure ; INT_PAY, interest payment as a percentage of total expenditure ; TOTAL_EXP, ratio of total expenditure as percentage of total state gross domestic product (SGDP)

ANNEXURE-I- End Table 1.5: Correlation Matrix, after applying Principal Component Analysis (PCA)

I_LEG_EFFI I_POLI_STAB I_RULE_LAW I_PROP_RIGHT I_STATE_INFRA I_FISC_GOV I_ECON_FRDM I_LEG_EFFI

1 -

I_POLI_STAB

.066 (.753)

1 -

I_RULE_LAW

-.186 (.373)

.528** (.007)

1 -

I_PROP_RIGHT

.586** (.002)

-.023 (.911)

-.253 (.223)

1 -

I_STATE_INFRA

-.745** .000

-.340 (.096)

-.240 (.248)

-.476* (.016)

1 -

I_FISC_GOV

.132 (.529)

.042 (.843)

-.078 (.711)

-.169 (.420)

-.039 (.853)

1 -

I_ECON_FRDM

.152 (.469)

.070 (.739)

-.041 (.845)

-.166 (.428)

-.053 (.802)

.994** .000

1 -

Notes: Figures in parentheses represent probability levels **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Abbreviations : I_LEG_EFFI ,Index of Legal Efficiency; I_POLI_STAB, Index of Political Stability; I_RULE_LAW, Index of Rule of Law; I_PROP_RIGHT, Index of Creditors’ Property Rights; I_STATE_INFRA, Index of State as a provider of necessary Infrastructures; I_FISC_GOV, Index of Fiscal Governance; I_ECON_FRDM, Index of Economic Freedom.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Rediscovering, Redefining and Rebuilding Manufacturing in the Education Sector a

S.K.Sumana, R.M.Belokarb, Harish Kumar Bangac, Pratik Belokard Electrical Engineering Department b,cProduction Engineering Department dChemical Engineering Department a,b,c PEC U&T Chanidgarh, dUICET Chandigarh a,b,c,d India

Abstract: Manufacturing industries worldwide have undergone dramatic changes in recent years and now demand more from graduating manufacturing engineers. The effects of globalization have forever changed the parameters for success in manufacturing. Our educational institutions must respond to these changes with innovation. Since most of the global economic crisis is developed in manufacturing organizations such as in Europe, Japan. This transition is made even more marked with the economic downturn taking place across the world. There is a strong movement of cost based manufacturing to offshore and low wage economies. The remaining onshore manufacturing activities now focus on innovative new processes and exceptional customer service. The technology and processes required for onshore manufacturing can be complex and challenges the existing skills of engineers and managers to continuously operate and change such systems. Educational bodies struggle to keep up to date. The paper provides a number of suggestions for strategic change to research and education in manufacturing in the future. Index Terms: Manufacturing, Strategy, Education, Pedagogy, Curricula I. Introduction Manufacturing education is in crisis. The evidence is striking and undeniable: Despite a consistently high United States unemployment rate for several years ranging between 8% and 10% from February 2009 through March 2021 as many as 600,000 manufacturing jobs have gone unfilled because of a shortage of skilled workers. The roots of this crisis started with a serious shortage of workers educated in Science, Technology, Engineering and Mathematics (STEM) fields, according to numerous reports. [1].A 2011 report authored by Deloitte and the Manufacturing Institute, “Boiling Point The Skills Gap in U.S. Manufacturing”[2-3], notes that the biggest areas of workforce shortage “are those that impact operations the most and require the most training.From technicians to engineers, the talent crunch in these critical areas is taking its toll on manufacturers’ ability to meet current operation objectives and achieve longer-term goals.” The SME Task Force on the Role of SME in Higher Education and members of SME’s Manufacturing Education & Research Community [3-8] has examined the state of manufacturing education. This research included a series of events discussing manufacturing education that engaged hundreds of stakeholders from industry, government and academia. II. Science, Technology, Engineering and Mathematics (Stem) Foundation Manufacturing must be portrayed as a vibrant industry that offers opportunities that are ample, creative, hightech and rewarding. Many students tend to be altruistic in their choice of careers, so educational programs should make it clear to students that manufacturing offers the chance to improve the world.[9] For example, students should be educated about the role manufacturing plays in the green revolution that saves energy, increases health, saves lives and creates a new economy. It is important to stress to students that there are multiple pathways, requiring different education levels, to a career in manufacturing. Figure 1 shows several options. However, a significant portion of the available manufacturing jobs require educational equivalency up to an associate degree. In order to develop technicians, there must be more support for technical education, especially in high schools. Students must have options for manufacturing paths that are hands-on and technical or academic in nature or, ideally, both. III. Manufacturing and Its Program’s Today, there is little agreement on the answer to that question. But if the crisis in manufacturing education is to be resolved, a better-defined manufacturing curriculum is needed to guide manufacturing education programs, with an eye toward accreditation.

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Fig.1 Institutional Pipeline for Manufacturing Knowledge Figure 2 gives the comparison between 2005 and 2025 of different countries in manufacturing education

Fig.2.Worldâ&#x20AC;&#x2122;s Talent Pools of Younger People This will enhance the understanding and value of certifications and degrees that individuals receive from accredited programs. The Department of Labor has taken a step toward defining the manufacturing skills workers need to be successful in 21st century manufacturing with its Advanced Manufacturing Competency Model [Figure 3].

Fig.3. Advanced Manufacturing Competency Model The model was developed in 2006 in collaboration with the Employment and Training Administration and updated in 2010 with leading industry organizations. The National Association of Manufacturers, the National

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Council for Advanced Manufacturing and the Society for Manufacturing Engineers took the lead in collecting feedback from their members to ensure that the updated model includes the most current processes and practices [9].manufacturing, such as mathematics. The middle levels define the technical competencies required by the manufacturing industry, such as production and quality assurance. The top levels define the competencies required by specific occupations within advanced manufacturing. This model serves as a framework for the National Association of Manufacturers-endorsed Manufacturing Skills Certification System [Figure 4], which is a system of stackable credentials from certification organizations applicable to all sectors in the manufacturing industry. These nationally portable, industry-recognized credentials validate the skills and competencies identified in the Advanced Manufacturing Competency Model [10]. But there has been a missing link between the skills defined in the Advanced Manufacturing Competency Model and the Manufacturing Skills Certification System. A model was needed to define the academic content needed for manufacturing education programs, whether they confer degrees or technical credentials. SME’s Center for Education developed and adopted a model, which is included in the “Curricula 2015,” [11-16] to provide a solution to this problem.

Fig.4 Endorsed Manufacturing Skills Certification System The Four Pillars of Manufacturing Knowledge is meant to be a standard for recommending curriculum topics and establishing accreditation criteria for manufacturing programs of all kinds. They also set important standards for accreditation of educational program [17]. Certifications and degrees that come from accredited programs are necessary for industry to verify and trust the level of skills and knowledge that certain individuals bring to the workforce. IV. Consistency and Quality of Manufacturing Manufacturing curricula vary greatly from school to school and program to program. While some flexibility is necessary, everyone educated in manufacturing should have a basic core of knowledge. Figure 5 shows the percentage of executive reporting high difficulty attracting and retaining qualified workers in different countries in between 2005 and 2025

Fig. 5 Comparison of Country wise Qualified Workers (Source: Deloitte search based on United National Department of Social Economic affairs) SME recommends that educators, industry, professional organizations and government work together to:

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• Use the Four Pillars of Manufacturing Knowledge and the Advanced Manufacturing Competency models as a foundation to build manufacturing curricula [18]. • Keep curricula current by monitoring trends in education and industry needs. • Use curriculum and instructional resources shared through the National Center for Manufacturing Education [19] and industry professionals V. Faculty Development In World-Class Manufacturing Education To develop faculty that can deliver an excellent manufacturing education, educators must: • Keep up to date on using new technologies. • Work with industry to understand current technical needs and update curriculum. • Collaborate with industry, professional organizations and government on projects such as design to build competitions and mentorships for both students as well as faculty. • Research, publish and participate in manufacturing journals and conferences. • Share best teaching practices. This includes in-service days and conferences, among other examples [20-25]. VI. Manufacturing Education Programs Manufacturing education is an essential priority and should be funded accordingly. All sources of possible funding should be sought to support manufacturing education, and investments need to be strategic and effective, with measurable results. Figure 6 shows the generation gap between young, medium and old in different countries during manufacturing

Fig.6. Generation gap by Age in 2005(Source: Deloitte search based on United National Department of Social Economic affairs) SME recommends that investments be guided by the Four Pillars of Manufacturing Knowledge, the Advanced Manufacturing Competency and the Skills Certification System models. Many educational programs and initiatives that demonstrate the excitement and rewards of manufacturing are now under way for young students. Among the efforts: National Engineers Week activities,[26] “Dream it. Do It.” campaigns, [27] SkillsUSA, [28] FIRST competitions [29]. VII. Conclusion It includes a discussion on the role of manufacturing in the global economy, the factors affecting the scope of manufacturing, and the current efforts to revitalize manufacturing in various parts of the world. It then presents an overview of the curricular models proposed to address the needs of the manufacturing industry. As an extension of the analysis, a recommendation is made on the key aspects of any manufacturing curriculum with an emphasis on innovation and entrepreneurship. Finally, a set of strategies are proposed for the enhancement of manufacturing education. They include the creation of a flexible degree program that emphasizes learning over teaching, development of a network of academic institutions around the globe to deliver the program, use of communication technologies to provide access to the program to anyone at anytime and anywhere in the world, and an outreach to future manufacturing professionals. VIII. References [1] [2] [3]

United States Department of Labor, Bureau of Labor Statistics, Unemployment rate data table “A Manufacturing Renaissance: Four Goals for Economic Growth.” Washington, DC: National Association of Manufacturers, 2011 “Boiling Point The Skills Gap in U.S. Manufacturing.” Deloitte and the Manufacturing Institute, 2011

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[4] [5]

[6] [7]

[8]

[9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]

“Made in North America.” A Deloitte Research Manufacturing Study. Deloitte Research, National Association of Manufacturers, The Manufacturing Institute and Canadian Manufacturers & Exporters, 2008 James P. Andrew, Emily Stover DeRocco and Andrew Taylor. “The Innovation Imperative in Manufacturing: How the United States Can Restore Its Edge.” The Boston Consulting Group, National Association of Manufacturers and The Manufacturing Institute, March 2009 “The Facts about Modern Manufacturing,” 8th edition. Washington, DC: The Manufacturing Institute, 2009 Robert D. Atkinson and Merrilea Mayo. “Refueling the U.S. Innovation Economy: Fresh Approaches to Science, Technology, Engineering and Mathematics (STEM) Education.” Washington, DC: The Information Technology and Innovation Foundation, December 2010 “Prepare and Inspire: K-12 Education in Science, Technology, Engineering and Math (STEM) for America’s Future.” Report to the President. Washington, DC: Executive Office of the President, President’s Council of Advisors on Science and Technology, 2010 “Help Wanted: Projections of Jobs and Education Requirements through 2018.” Washington, DC: Georgetown University Center on Education and the Workforce, 2010 “Pathways to Prosperity: Meeting the Challenge of Preparing Young Americans for the 21st Century.” Pathways to Prosperity Project. Cambridge, MA: Harvard Graduate School of Education, February 2011 “Rising Above the Gathering Storm, Revisited: Rapidly Approaching Category 5.” National Academy of Sciences, National Academy of Engineering and Institute of Medicine. Washington, DC: The National Academies Press, 2010 “America’s Changing Workforce: Recession Turns a Graying Office Grayer.” A Social & Demographic Trends Report. Pew Research Center, September 2009 Facts about Manufacturing. Washington, DC: National Association of Manufacturers Hugh Jack et al. “Curricula 2015: A Four Year Strategic Plan for Manufacturing Education,” June 2011 “Bridging the Skills Gap: How the Skills Shortage Threatens Growth and Competitiveness… And what to do About It.” Alexandria, VA: Public Policy Council, American Society for Training & Development, Fall 2006, “Changing the Conversation: Messages for Improving Public Understanding of Engineering.” National Academy of Engineering. Washington, DC: The National Academies Press, 2008 “Criteria for Accrediting Engineering Programs, 2012–2013.” Baltimore, MD: ABET, October 2011 “Advanced Manufacturing Competency Model.” Washington, DC: United States Department of Labor, Employment and Training Administration National Center for Manufacturing Education (NCME), www.ncmeresource.org “Vision 2030 Reveals Workforce Development Needs.” ASME Today, May 2011 Center for Pre-Collegiate Engineering Education, University of St. Thomas Engineering and technology minor, University of Dayton Aditya Johri and Barbara M. Olds. “Situated Engineering Learning: Bridging Engineering Education Research and the Learning Sciences” Journal of Engineering Education, 100 (1), January 2011, pp. 151-185 “Going the Distance: Online Education in the United States, 2011.” Newburyport, MA: Babson Survey Research Group, Babson College, November 2011 Discussion with Paul Nutter, Ohio Northern University, June 4, 2011, referring to Tooling University National Engineers Week, www.Eweek.org Dream It! Do It! The Manufacturing Institute, www.Dreamitdoit.com Skills USA, www.SkillsUSA.org USFIRST, www.USFIRST.org

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Analysis and Forecasting of Drought by Developing a Fuzzy-Based Hybrid Index in Iran 1

ParisaLakhayeRizi, 2Reza Moghaddasi, 3AlirezaEghbali M.Sc. Student, 2Assistant Professor, 3Assistant Professor 1,2 Department of Agricultural Economics, Science and Research Branch, Islamic Azad University, Khouzestan-Iran. 3 Department of Economics, Payame Noor University, Iran, __________________________________________________________________________________________ Abstract: Drought is the most important and destructive climate phenomenon which is usually of importance in a regional scale. Therefore, this study offers a fuzzy-based hybrid index in order to analyze the regional drought in Abadan and khoramshahr, Khuzestan, Iran. Influencing all aspects of human activity, drought does not have a comprehensive definition and an appropriate and general index to explore it. Consequently, in order to develop a model to evaluate and analyze drought, the fuzzy model has been used. The application of fuzzy logic to examine drought in Abadan-khoramshahr station demonstrated that fuzzy logic enables us to examine drought more accurately and appropriately because it takes into account the type of product (wheat or dates) in calculating the probability of drought. In furtherance of this aim, the fuzzy function related to the standardized Precipitation Index (SPI) and the standardized evapotranspiration index (SEI) have been combined and a new indicator called the standardized evapotranspiration and Precipitation index (SEPI) was developed. In the final fuzzy model, 81 rules have been utilized. In this study, the annual data of wheat and dates from 1994 to 2012 have been utilized (sometimes 2013 data have been used), and on this basis, the results of the model revealed that severe and continued droughts have occurred in 1999, 2007 and 2009 and the probability of drought for wheat and dates was 64.29 and 57.14 percent respectively in this period. Key Words: Water Requirement, Probability of Drought, wheat, dates, SEPI. JEL Classification: Q12, O13, R11, C15. __________________________________________________________________________________________ 1

I. Introduction Agriculture as one of the most important sections of Iran's economy has a large contribution to GPP, non-oil exportation, and employment (Shams, 2005). Due to the huge population growth and the increased demand for agricultural and animal products, agriculture and animal husbandry have gained much more importance, and developing more efficient production procedures and supporting domestic producers has become more crucial (Enjolras et al, 2012). Moreover, agricultural activities have bilateral risks, affecting both producers' behavior and macro-agricultural policies. Among different risks involved in this section, some risks cannot be controlled and have a wide range of consequences, an important instance of which is drought(De Janvry et al, 2014). Drought is considered as a creeping phenomenon since a large part of its impact is not observed in a particular period; hence planning to reduce its damages is difficult. In comparison with other natural disasters, drought is greater with regard to severity, spread, human losses, economic damage, and long-term consequences, posing a serious threat to sustainable development (Leblois and Quirion, 2013). Therefore, utilizing efficient methods to identify and forecast risk-producing factors in different sectors of agriculture (such as drought) can assist policy makers to adopt appropriate policies (Anton, 2013). The importance and function of calculating the risk of drought in Several areas have been dealt with in a wide range of studies, such as Kim et al (2013); Ramsay et al (2013); G贸mez and Blanco (2012); Gil et al (2011); Altmana et al (2009); Waterbury and Mark (2008); Akcaoz and Ozkan (2005); Skees et al (2002) and Wright and Hewitt (1994) studies. Due to the climate conditions in Iran and the importance of agriculture sector in the prosperity of provinces, particularly those having revealed agriculture-related comparative advantages, the consequences of drought in the southern cities of Iran, and the strategic nature of wheat and dates in Abadan and Khoramshahr, investigating the probability of drought based on the type of products is of high importance. II. Agriculture in Khuzestan Khuzestan Province has an area of 67282 square kilometers and is located in the south-western of Iran. It has mild summers and cold winters in mountainous areas and has a semi-desert climate in foothills. Having access to open water in southern coasts and numerous huge rivers, which have been the habitat of ancient Iranian tribes, is an important advantage of this province. A. Abadan and Khoramshahr Abadan has an area of 576 square kilometers and is located in the south-west of Khuzestan and across the

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Persian Gulf. After Ahwaz, Abadan is the second most important city of Khuzestan. The strategic importance of the Oil Refinery and having borders with Iraq make Abadan one of the most significant cities in Iran and the Middle East. Abadan is located between the Persian Gulf and Arvand and Bahmanshir rivers and has a hot and humid climate. This city is among the low-rainfall areas and usually receives fleeting showers, and the type of soil in this city is quite appropriate for agriculture. Most agricultural activities are devoted to cultivating and raising palmtrees. The water of Bahmanshir and Arvand rivers are used to irrigate these trees. Therefore, we should bear in mind that rainfall has utmost importance in the agricultural activities of this region, and hence water Shortage and drought severely damage that agricultural activities. In the south-west of Khuzestan and has a dry and humid climate. Irrigation resources for the farms of this city are Karun and Arvand rivers, and the farms located in the south and south-west of these rivers. Due to the special geographical position of Khoramshahr, it is potentially and actually capable of enhancing its agricultural activities. Wheat and dates are prominent products of this city which are both cultivated by means of irrigated farming. Due to high temperature and low rainfall in this region, like Abadan, the negative consequences of drought can do a serious damage to its farms and palm trees. Therefore, the ability to control agricultural risks in these two cities is of high importance. B. Wheat Wheat is one of the most salient agricultural products. In societies like Iran, wheat has crucial nutritious importance, and the social welfare of low and middle classes is heavily dependent on this product (Khousravipour et al, 2011). Like many other countries, wheat bread is peopleâ&#x20AC;&#x2122;s most basic daily food. Wheat is one of the most important agricultural products in Khuzestan. Ten percent of the total amount of wheat produced in Iran is cultivated in this province (Hakimi, 2012). Wheat is cultivated by means ofrain-fed and irrigated methods. Research has shown that the amount of wheat produced through rain-fed method is directly related to the amount and distribution of rainfall. Therefore, rainfall has a strong impact on wheat production. This will increase the production risk particularly in Khuzestan facing the risk of drought (Shamohammadi et al, 2005). Consequently, drought and adverse climate conditions bear a negative effect on wheat production. Hence, managing drought risks in wheat production is of prime importance, particularly in areas with a hot climate. C. Dates Dates, a tropical plant, is one of the oldest and most strategic agricultural products. Containing a lot of sugar, vitamins, and minerals, dates have great nutritious value. Dates are raised in arid and semi-arid areas having hot and long summers (Abdoullahi and Abedini, 2010). They are reaped from August to September. Based on global statistics, Iran has the second largest cultivation area of dates in the world (Koshteh and Kamalian, 2004). Dates are the third most 'important horticultural products in Iran and is cultivated in 13 provinces. With regard to the amount of production, Khuzestan stands third in Iran (after Kerman and Bushehr). Although dates are generally raised in arid and semi-arid areas, palm trees need sufficient water to grow and production of high quality fruits. Therefore, increase in the temperature and decrease in relative humiditynegatively affects the performance of palm trees (Mohammadrezaei et al, 2009). Consequently, in order to utilizing maximum capacity of date's production, enough attention should be devoted to water shortage, drought and their managements. III. Drought Risk Vulnerability to drought is a function of its nature, size, and severity. On the other hand, human beings cannot forecast many events properly. Therefore, there is always a degree of uncertainty and a degree of inevitable risk (Hungsoo et al, 2013). Drought management has not experienced a huge progress in many parts of the world and many reactions to this phenomenon have been traditional. Drought risk management or the set of measures taken before the drought minimize the shock experienced during this phenomenon. This issue has not been fully attended in developing countries (Blanco et al, 2013). Crisis management has become less valid; therefore, many governments try to gain more information about appropriate methods of risk management in order to mitigate the damages caused by drought and the negative consequences of possible droughts in future (Anton et al, 2013). A. Theoretical Foundations of Drought Management Drought is a natural danger and a big disaster, posing many problems for countries. Some of the instances of the most severe droughts in the 20th century include the drought in China in 1907, the Soviet Union in 1922, India in 1967 and Africa in 1975 (Kim et al, 2011). Many factors contribute to the occurrence of drought in different parts of the world;the common features of all of them are the potential amount of evapotranspirationand the amount of rainfallwhich make the crops water requirement difficult. In addition, droughts pose numerous economic problems for farmers (Campbell et al., 2011). B. The Probability of Drought Determining the time at which droughts start and end is difficult because this phenomenon is creeping and its consequences may hit an area over a period of time and continue for some time (Ward, 2014). Moreover, the

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negative effects of a drought in a wide area seem to be less than those of other natural disasters. On the other hand, more than 90 percent of Iran is located in arid areas with low water supplies, hence forecasting the probability of droughts is an important concern of policy makers (Eyvasi et al, 2013). Almost all areas in Iran are susceptible to the negative effects of drought. They constantly face drought. Central, western, and southwestern areas in Iran are more vulnerable to drought because the amount of rainfall has been fluctuating over the past years. Therefore, forecasting the probability of the occurrence of drought can be quite beneficial for these regions. C. Methods of Calculating the Probability In order to identify drought and its negative environmental effects, many indexes such as rainfall, average temperature, soil humidity andevapotranspiration are considered. There are many variables involved in drought, so that there are different definitions posed for this phenomenon. What is of prime importance is the role played by factors such as rainfall andevapotranspiration. C.1. Standardized Precipitation Index (SPI) This indicator, proposed by McKee et al. (1993) for the first time, is a powerful instrument to analyze rainfallrelated data. This indicator is aimed at analyzing the amount of rainfall so that we can compare rainfall in different areas (Slahedin et al, 2014). We may calculate this indicator by comparing the aggregate amount of rainfall in a particular period of time in a particular region with the average rainfall in the same duration for all of the statistical periods. This indicator is measured for each particular region based on the long-term rainfall data, observing gamma distribution. The normalized SPI equation is as follows: (1)

SPI 

(2)

SPI 

xi  x

 In this equation,  represents the standard deviation of rainfall, x is represents the amount of rainfall, and x is the average of accumulative rainfall (Alemaw and Kileshye-Onema, 2014). C.2. Standardized Evapotranspiration Index (SEI) This indicator's equation is as follows: ETi  ET

 In this equation, ETi represents the evapotranspiration at i , ET represents the average amount of accumulative evapotranspiration,  is the standard deviation of evapotranspiration (Labedzki and Kanecka-Geszke, 2009). C.3. Standardized Evapotranspiration and Precipitation Index (SEPI) The SEPI indicator has been introduced by Vicente- Serrano et al. (2010). According to fuzzy logic, this indicator is a combination of SPI and SEI and has all their features because the use of both of these variables is necessary to measure drought and neglect of either of them will negatively influence the results of the study (Sayari et al, 2013).

Table 1: The Classification of Drought based on SEPI Description

Classification

Too Severe Drought

Severe Drought

1.5 to 1.99

Average Drought

1 to 1.49

Mild Drought

0.5 to 0.99

Normal

-0.49 to 0.49

Mild Wet Year

-0.99 to -0.5

Average Wet Year

-1.499 to -1

Severe Wet Year

-1.99 to -1.5

Too Severe Wet Year

< -2

Source: Vicente - Serrano et al. (2010) In the calculation of SPI the only input is rainfall. Therefore, using SPI to analyze the severity of drought in arid areas, in which the differences in the temperature, evapotranspiration are great, leads to a big error in the analysis of drought. Hence, the combination of the two indexes removes the above-mentioned limitations. After calculating SPI and SEI and using the classified linguistic variables and due to advantages of fuzzy logic in the combination of linguistic variables, SPI and SEI are combined based on fuzzy logic, and a combined indicator has created (Potop and Mozny, 2011).

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Table 2: SPEI Values for Different Levels of Drought Description Mild Wet Year (1) Mild Wet Year (2) Mild Wet Year (3) Average Wet Year (1) Average Wet Year (2) Average Wet Year (3) Severe Wet Year (1) Severe Wet Year (2) Severe Wet Year (3) Too Severe Wet Year (1) Too Severe Wet Year (2) Too Severe Wet Year (3)

ď ś

Classification 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Description Mild Drought (1) Mild Drought (2) Mild Drought (3) Average Drought (1) Average Drought (2) Average Drought (3) Severe Drought (1) Severe Drought (2) Severe Drought (3) Too Severe Drought (1) Too Severe Drought (2) Too Severe Drought (3)

Classification -6 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5

If SPEI equals zero, climate conditions are normal Source: Vicente - Serrano et al. (2010)

C.4. Fuzzy Logic Fuzzy logic was introduced in the framework of the fuzzy set theory proposed by Professor LotfiZadeh. Fuzzy logic includes three distinct stages, namely (1) fuzzy making stage, (2) accumulation stage, and (3) un-fuzzy making stage (Esfahanipour and Aghamiri, 2010). All these stages occur in the framework of fuzzy inference systems. They are popular calculation frameworks based on fuzzy sets and "if-then" rules, which have successful applications in many domains such as economics, engineering, etc (AskariZadeh, 1965). IV. Empirical Results A. Drought and Its Relation with Net Water Requirement There are four kinds of drought, namely metrological drought, hydrological drought, agricultural drought, and socio - economic drought. In this research, we are mainly concerned with agricultural drought. Therefore, it is necessary to propose a comprehensive definition of this type of drought at the beginning of this section. Agricultural drought takes place when the humidity stored in roots of the plants is not enough for those to survive between two rainfalls. It should be noted that even when the soil and the weather contain humidity, the plants may be exposed to drought, which is caused by a sudden increase in the temperature. Accordingly, the drought risk for wheat and dates will be different. Therefore, it is crucial to pay enough attention to sudden temperature changes affecting the degree of evapotranspiration in plants. Hence, there seems to be a significant and logical relationship between drought andnet agronomic water requirement. B. Comparing the Net Water Requirements of Wheat and Dates The decrease of surface and underground water and rainfall in any regions can have a huge negative impact on agricultural activities. As a matter of fact, drought makes plants unable to satisfy their water requirements and this undermines their performance. Therefore, comparison of the wheat and date's net water requirements in the recent years can offer us fresh insights about the drought risk of these two products. Efficient planning to tackle drought requires gaining information on net water requirements of plants. Due to the prime importance of wheat and dates for Khoramshahr and Abadan respectively, we investigate the net water requirements of these two products (Figure.1). Figure 1: Comparing the Net Water Requirements of Wheat and Dates 800 700 600 500 400 300 200 100 0 94 95 96

98 99 00 01 02 03

Average Water Requirements of Wheat - - -

06 07 09 10

12 13

Average Water Requirements of Dates

Source: The Finding of the Studies In these two cities, a part of net agronomic water requirement are met by dint of rainfall that usually occurs in cold seasons during which the level of evapotranspiration is low and plants don't require any water. Therefore,

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Rain watercan be added to underground and surface water. In warmer seasons of the year when the level of evapotranspiration goes up and plants request more water for their growth, a large part of waterrequirements are met by rainfall and a small part is satisfied through irrigation. In Khoramshahr, wheat is mainly irrigated by underground water, Karun River, and other sources of surface water. Palm trees in Abadan are also mainly irrigated by underground water, the tide of Arvand Rood, Karun, and, Bahmanshir, and other sources of surface water. Comparing the average net water requirement of wheat and dates between 1999 and 2013 reveals that the net water requirement of wheat is more than that of dates, and another reasonable and logical indication of the higher net water requirement of wheat is that the growth period of wheat is 220 days on average, while this period for dates is 120- 200 days on average. C. Estimating the Probability of Drought Risk C.1. Estimating the Standardized Precipitation Index (SPI) According to the results of SPI, a large number of years in the period investigated in this study have been gripped with drought with varying degrees in different years. The important point is that drought was very severe in 1999, 2007, and 2009. C.2. Estimating the Standardized Evapotranspiration Index (SEI) The crucial importance of SEI lies in the fact that it provides a more pragmatic concept than what drought does. The degree of agricultural drought for different plants may vary in accordance with their features and this should be taken into account in the analysis. The results of drought analysis based on SEI reveals two basic points. The first point is that, as it was expected, the climate conditions for wheat and dates were not symmetrical in some years such as 2001 and 2004 and hence, the results of the indicator are not symmetrical. The second point is that the results of SEI are different from those of SPI, which is evident in light of the fact that calculation criteria of these two indexes are different. Therefore, relying on the results of each of the indicators separately will deviate the results of the analysis. Consequently, the dependency of the applied results based on the calculation of the probability of drought is unquestionable. In order to improve the precision of calculating the probability of drought, SEPI is introduced in the next section. C.3. Estimating the Standardized Evapotranspiration and Precipitation Index (SEPI) After determining all of the stages of fuzzy calculations and the fuzzy sets, the related computer program was designed in MATLAB software. On this basis, the input would be SPI and SEI values and the output would be SEPI values. After determining the functions of SPI and SEI, a combination of the two indicators with different weights in accordance with the experts' views (the weight of rainfall is set as two times more than that of evapotranspiration), the fuzzy function of the levels of drought for SEPI were considered and the final fuzzy model was produced with 81 rules. Finally, in order to use the results of the fuzzy model, the final stage of this model, is the un-fuzzy making of the output, SEPI was determined for both wheat and dates. Then, the probability of drought was calculated for wheat and dates separately based on SEPI. The result of this analysis is demonstrated in Table 3. Table 3: The Result of the separate SEPI probability for wheat and dates Description Too Severe Drought Severe Drought Average Drought Mild Drought Normal Mild Wet Year Average Wet Year Severe Wet Year Too Severe Wet Year Sum Total probability of Drought Total probability of Wet Year

SPEI Probability of Wheat 7.14 28.57 21.43 7.14 0 7.14 14.29 14.29 0 100 64.29 35.71

SPEI Probability of Dates 7.14 28.57 14.29 7.14 0 14.29 14.29 14.29 0 100 57.14 42.86

Source: The Finding of the Study Table 3 demonstrates the probability of different climates. In addition, the percentage of the aggregate probability of drought based on SEPI that equals the sum of probabilities of very severe, severe, average, and mild drought is 64.29 percent for wheat and 57.14 percent for dates. V. Conclusions Most natural phenomena have elements that cannot be easily forecasted. Forecasting is possible if some information about their past is available. An important instance of such phenomena is drought having hugely destructive consequences. On this basis, this study offers a fuzzy-based hybrid index in order to analyze the regional drought in Abadan and khoramshahr, Khuzestan, Iran. Our Findings revealed that severe and continued droughts have occurred in 1999, 2007 and 2009 and the probability of drought for wheat and dates was 64.29

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Influence of tool pin profiles on mechanical properties of FSP processed aluminium 6063 Chandan Deep Singh1, Ripandeep Singh2*, Napinder Singh3, Jaimal Singh Khamba4 Assistant Professor, Department of Mechanical Engineering, Punjabi University Patiala 2 Assistant Professor, GZSPTU Campus, Bathinda 3 Research Scholar Department of Mechanical Engineering, Punjabi University, Patiala 4 Professor, Department of Mechanical Engineering, Punjabi University Patiala ____________________________________________________________________________________________ Abstract: FSP provides intense plastic deformation of material especially in processed zone which causes material mixing, and thermal exposure, resulting in significant micro structural refinement and enhancement of mechanical properties, densification, and homogeneity of the processed zone due to the stirring action of non consumable tool profile (mostly circular pin profile) In this research effort is made to find out the effect of other tool pin profile on mechanical properties (impact strength and Rockwell hardness) of aluminium material (6063). There were four tool profiles used (circular, pentagonal, threaded and square tool pin profile) of HSS material having Rockwell hardness 58.All the parameter were kept constant and aluminium sample plates (150*100*6 mm) were processed under vertical milling machine with 1000 number of rotations and feed rate 19mm/min. All these parameter were kept constant during all four profiles .CNC Vertical milling machine used for processing. Final results carried out based on mechanical testing of FSP processed aluminium 6063 plates. All specimens processed with four tool pin profile show better result than base plate. But out of four pin profile pentagonal pin profile show better results than other three pin profiles. Result show that microstructure were fine grains without any cracks in processing zone by using pentagonal tool pin profile with better mechanical properties (micro hardness, impact strength and Rockwell hardness), because pentagonal pin profile provided more stirring action and crystallizations in nugget zone. Better results by profiles are as (1) pentagonal tool pin profile (2) square tool pin profile (3) circular tool pin profile (4) threaded pin profile. Keywords: Friction stir processing, Tool pin profiles, FSPed aluminium mechanical properties ____________________________________________________________________________________________ 1

I. Introduction Friction stir processing (FSP) is solid state process in which a non consumable stirring(rotating) tool plunged into work piece up to half thickness, which causes intense plastic deformation, material mixing, and thermal exposure, resulting in refinement of micro structural, enhancement of mechanical properties and homogeneity of the processed(nugget) zone. In friction stir processing, a rotating tool with pin and shoulder is (made from material like HSS, mild steel etc).inserted up to 1/2 thickness of workpiece, and shoulder touches the workpiece surface.

Fig.1.1: friction stir processing principle The FSP technique is emerging as a very effective solid-state processing (material remains in plastic state) technique that can provide localized modification and control of microstructures of soft materials in the near-surface layers of processed metallic components. In the relatively short duration after itâ&#x20AC;&#x2122;s invention, increasing applications are being found for FSP in the fabrication, processing, making composites etc. Friction stir process is quit simple process

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which can well controlled by using numerical controlled machines. The use of FSP generates significant frictional heating and intense plastic deformation, thereby resulting in the occurrence of dynamic recrystallization in the stirred zone (SZ) or nugget zone (NZ). Furthermore, the FSP technique has been used for the fabrication of a surface composite on aluminum substrate and the homogenization of powder metallurgy (PM) aluminum alloys, metal matrix composites, and cast aluminum alloys. Compared to other metalworking techniques, In FSP, the major processing parameters are the tool rotating rate, the tool traversing speed and proper tool pin profile there are different types of tool profiles. The intense plastic deformation around the tool and the friction between the tool and the work piece both contribute to the temperature increase in the stirred zone (SZ).SZ is processing zone where tool starting stirring (rotating). Generally, an increase in the ratio of the rotation rate to the traversing speed will increase the peak temperature in FSP. In the present thesis it is try to find out proper tool pin profile in friction stir processing. In the present study, the effect of the tool pin profile on mechanical properties is to be examined that how these profile effect mechanical properties of aluminium 6063. II. Experimentation Friction stir processing of the base plate (6063 Al) was performed on a CNC vertical milling machine, (made by HAAS automations INDIA) a using a FSP tool, as described earlier. Before FSP, the plate having dimension 150*100*6mm, was gripped in the fixture. The fixture containing duly gripped base plate was placed and tightened with the bed of CNC vertical milling machine.Comands are given to machine with spindle speed of 1000rpm and feed rate 19mm/min.Tool depth given 3mm in work piece surface. Tool firstly start rotating in air and then plunged into work piece where tool start stirring at 1000rpm (nugget zone). All the procedure repeated by each four profiles i.e. pentagonal, rectangular, threaded and circular tool pin profile at same constant parameters. Tool holder holds tool by pneumatic action, so it was very easy to change tool again and again during operation. Four plates were processed by four tool pin profiles with one each. Results obtained are discussed in next section.

Square pin profile

pentagonal pin profile circular pin profile Fig.2.1: different tool pin profile used

threaded pin profile

Table.2.1: process parameter for each profile Tool profile

Process parameter

Pentagonal tool pin profile

Spindle speed=1000 feed rate=19mm/min

Threaded tool pin profile

Spindle speed=1000 feed rate=19mm/min

Circular tool pin profile

Spindle speed=1000 feed rate=19mm/min

Square tool pin profile

Spindle speed=1000 feed rate=19mm/min

In this study the FSP was performed on plate made of 6063 aluminium alloy. Chemical compositions is as following Table.2.2: base material composition Element Cu Mg Si Fe Magnese Ni

wt% 0.082 0.619 0.600 0.350 0.044 0.006

Zn Pb Tn Titanium Cr Al

0.061 0.046 0.024 0.015 0.007 97.94

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A. Characterization and testing of samples A.1 Izod impact strength testing Impact tests provide information on the resistance of a material to sudden fracture where a sharp stress riser or flaw is present. In addition to providing information not available from any other simple mechanical test, these tests are quick and inexpensive. The data obtained from such impact tests is frequently employed for engineering purposes. Impact testing on FSP aluminium 6063 conducted because Al 6063 is domestically used material (used in window, door frame and architecture engineering) where impact load always act on material. So it is necessary to find out impact strength of material before and after FSP. In present study izod test conducted on al 6063 specimens as shown in following diagrams.

Fig.2.2: Al 6063sample prepared for impact testing

Fig.2.3: striking position and specimen fracture after izod impact test Various standard impact tests are widely employed in which notched specimens are broken by a swinging pendulum. In this testing IZOD impact testing used to find out the impact strength. Four this purpose specimen cut into desired shape by hand hacksaw and notch cut up to 2mm in processing zone for weaken the processing zone area so that impact strength of processed area can be measured as shown in above diagram. Above diagrams show standard dimensions of specimen for hold on vice and R.H.S. diagram show specimen after impact testing. Results of test for each profile are discussed in next chapter. A.2 Rockwell hardness testing To check the Rockwell hardness processed specimens grinded into 10*10 mm sizes. 1kg of load applied on plate surface after which different results were obtained which are discussed in next chapter. Pyramid shape indenter was used for testing size of 1/6â&#x20AC;? ball.

Fig.2.4: Pyramid shape indenter and indenter marks in processed zon

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III. Results of impact strength Aluminium specimen both processed and unprocessed were tested for find out impact strength variation in which test show that material bends, deformed and does not completely broken as brittle material (cast iron). Impact strength of processed specimens was measured in processed zone area by providing notch in processed zone (as shown in previous chapter) and base plate also. After results it has been found that processed zone having more impact strength (12.53) than base plate (12 joule).It is clear that there is also increase in impact strength of FSP processed specimen Table.3.1: impact strength results Tool profile Pentagonal pin profile Square pin profile Threaded tool profile Circular tool profile Base plate

Impact load (kg) 5 5 5 5 5

Impact strength (in Joule) 12.53 12.43 12.22 12.11 12.00

Out of processed specimen, pentagonal tool pin profile specimen show more impact strength (12.53 joule) than other three profiles as shown in table 3.1It is also cleared that FSP processed material having fine grained microstructure having maximum mechanical properties than unprocessed base plate of aluminium 6063. A. Rockwell hardness testing result Friction stir processed sample plates show more hardness than base metal because processed plate having fine grain crystallized homogeneous microstructure which is responsible for more hardness than unprocessed base plate. Rockwell hardness of each processed specimen was measured in center and sides and mean of three values was evaluated.It was observed that side of nugget zone sides having slightly more Rockwell hardness than its center as shown in table.4.4 out of four tool pin profile pentagonal tool profile processed plate specimen having maximum Rockwell hardness HRB 35.5. Table.3.2: Rockwell hardness result Tool pin profile

Parameter(load)

Results(hardness)

Pentagonal tool pin processed specimen

1kg

HRB 35.5

Square tool pin processed specimen

1kg

HRB 32

threaded tool pin processed specimen

1kg

HRB 30

circular tool pin processed specimen

1kg

HRB 29

Base plate specimen

1kg

HRB 24

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Finally Best results obtained by profiles (in all tests) are as Pentagonal tool pin profile square tool pin profile threaded tool pin profile

circular tool pin profile

IV. Conclusion First of all it is clear that all FSP processed specimen gives maximum mechanical properties than Al 6063 base plate (unprocessed). Secondly, effect of each tool pin profile examined by conducting the test on specimens, processed by each profile. Results show that pentagonal tool pin profile processed plate gives best result in mechanical tests. Reason behind that pentagonal tool pin profile having sharp edges with which are responsible for more stirring action and crystallizations in nugget zone hence fine grain microstructure. There is slightly increase in impact strength 12.53 joule with pentagonal tool pin profile than base plate and remaining three tool pin profiles (table 3.1). Rockwell hardness 35.5 HRB of pentagonal tool pin profile processed plates than base plate Al 6063

 



References [1] [2] [3] [4] [5] [6] [7] [8]

[9]

[10] [11] [12] [13]

Aydın H, Bayram A, Uguz A, Akay KS(2009),“Tensile properties of friction stir welded joints of 2024 aluminum alloys in different heat-treated-state”. vol 38, Mater Des, pp: 2211–2221 Aldajah SH, Ajayi, Fenskeb GR, Davidc S. (2009), “Effect of friction stir processing on the tribological performance of high carbon steel”. journal of Wear 2009, pp: 350–355. B. Zahmatkesh, M.H. Enayati(2010),“A novel approach for development of surface nanocomposite by friction stir processing”.Materials Science & Engineering A,pp:125. Buffa G, Fratini L,Pasta S, Shivpuri R (2008),“On the thermo-mechanical loads and the resultant residual stresses in friction stir processing operations”. Volume 57, CIRP Annals - Manufacturing Technology,pp: 287–290. Douglas C. Kenneth S. Vecchio (2007), “Thermal history analysis of friction stir processed and submerged friction stir processed aluminum”.vol 465, Materials Science and Engineering A ,pp: 165–175. Darras B.M,Khraisheh M.K,Abu-Farha F.K,OmarM.A(2007),“Friction stir processing of commercial AZ31 magnesium alloy”. vol 191, Journal of Materials Processing Technology 191 (2007), pp: 77–81. Essam R.I. Mahmouda,b, Makoto Takahashib, Toshiya Shibayanagib, Kenji Ikeuchib(15 January 2010), “Wear characteristics of surface-hybrid-MMCs layer fabricated on aluminum plate by friction stir processing”. vol 268, Wear journals 268, pp: 1111–1121. Jerome S, Govind Bhalchandra S. Kumaresh S.P, Babu, Ravisankar B(2007),” Influence of Microstructure and Experimental Parameters on Mechanical and Wear Properties of Al-TiC Surface Composite by FSP Route” Vol. 11.Journal of Minerals & Materials Characterization & Engineering, No.5, pp.493-507. Johannes L.B,Charit I, Mishra R.S,Ravi Verma(5 February 2007),“Enhanced superplasticity through friction stir processing in continuous cast AA5083 aluminum”. Vol 23, Materials Science and Engineering A ,no 464,pp: 351–357. Mishra R.S., Z.Y. Ma, I. Charit (2002), “Friction stir processing: a novel technique for fabrication of surface’’.A review journal Materials Science and Engineering A341, pp: 307-/310. Mondal A.K, Kumar S.(24 December 2008),“Dry sliding wear behaviour of magnesium alloy based hybrid composites in the longitudinal direction”.Wear 267,pp: 458–466. T.R. mcnelley.( 26 November 2007),“Recrystallization mechanisms during friction stir welding/processing of aluminum alloys”. Vol 8,Scripta Materialia 58,pp: 349–354 Wanchuck woo, hahn choo, donald w. Brown, and zhili feng(2007),”Influence of the Tool Pin and Shoulder on Microstructure and Natural Aging Kinetics in a Friction-Stir-Processed 6061–T6 Aluminum Alloy”. Vol 77,Minerals, Metals & Materials Society and ASM International.pp:71

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Online sales promotions of Grocery and other FMCG products in Chennai Entity Alexander CVJ Victoria* and Dr. M. Ganesan** *Research Scholar, Department of Management Studies, Bharath University, Chennai, Tamil Nadu, India. **Professor, Research Guide, Department of Management Studies, Bharath University, Chennai, Tamil Nadu, India. ______________________________________________________________________________________ Abstract: Along with the explosion of Internet user, Internet has been considered as the new channel for companies implementing their sales promotion activities. Online Sales promotions are generally looked at as tools that undermine the brand; yet a tool that is necessarily meant to speed up sales by attractive promos. Consumer online sales promotion in Chennai entity takes up a large share of the total marketing expenditure despite which it remains an area that still attracts attention as an essential component of the promotion mix meant to increase short term sales. It is therefore not surprising that most of the Chennai marketers resort to sales promotions to attract the competitor's market share. Consequently, this paper seeks to offer insight into how popular Chennai online promotions (price-discount, coupon and free shipping) influence consumer's quality perception and purchase intentions. Moreover, brand awareness was expected to moderate the relationship between promotion and consumer responses. Findings from this study will be able to provide useful knowledge for online sellers to choose appropriate sales promotion tools to successfully induce consumer's purchase intentions. Keywords: Coupons, FMCG, Grocery, Online sales, Price-off, Promotions. _________________________________________________________________________________________ I. Introduction Buying electronics goods including camera, laptops, computer peripherals, apparels, flowers and gifts are quite a common phenomenon for net savvy consumers. However, now get ready to receive the groceries like vegetables, food items and other FMCG products like shampoo, soap clothing and accessories at your doorstep. After touching apparels and personnel items now Chennai retailers are gearing up to facilitate grocery and other FMCG products through power of online. As of now there are very few retailers providing grocery including other FMCG products, however the trend would be catching up as grocery and other FMCG products are big opportunity for retailers. According to the data available the food and grocery segment constitutes about 50 per cent of the total 10 lakes Chennai retail market. Here are the reasons why grocery and FMCG market will thrive by coming online:  Increasing PC and Internet Penetration  Retailers are looking for newer verticals and grocery & FMCG have huge market and potential.  More than 60 percent of the consumers in FMCG are multinational. So, they will not have any hesitation in coming online.  Today’s generation prefers to shop online rather than drive to physical store and most of the FMCG products are being purchased by age group of 18 to 35.  Discount offer will certainly drive customers to shop online. We have witnessed the success of apparel, electronics, computer peripherals and group buying through discounts.  Online availability of grocery and FMCG product would help consumers to save time and avoid rush and hassle of processes like billing.  Investment of retailers in offline medium is high, however in online medium they do not have to go for huge investment. FMCG companies and Chennai retailers have started using the online medium not only for brand promotion but also for sales. However, it is a difficult category to break into in terms of online sales, but many retailers are making best efforts to do so. Chennai Marketing and sales Companies, like: 1. Chennai hyper market 2. Maligakadai chennai 3. Saravana stores 4. Medplus pharmacy and beauty 5. Chennai Online grocery 6. Landmark Chennai.

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are targeting Internet savvy consumers to expand their reach through the internet.

II. A literature survey on online sales promotions Here is an example how grocery and FMCG products become saleable after a boom in telecom sector we have seen consumers make order on phone to local retailers (Sekar, Chennai) and retailers deliver the order to the customer. In the same fashion online process will also work. Speaking about online grocery and FMCG product Dinesh, MD, Pachha.in said â&#x20AC;&#x153;Online grocery and FMCG products have challenges and opportunities too. On one side it is difficult to meet supply chain requirement, tough operation and short delivery commitment and thrive on thin margins. The demand of online grocery is growing in urban population are aware about convenience they get. This is good time for entrepreneurs to foray in to by considering FDI norm get relaxed in

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times to come”. Apart from all advantages of online availability of grocery and FMCG products there are some challenges. Here we have outlined the challenges:

Pointing out the potential of grocery and FMCG products on online platform, Bala subrmaniam, BDM, Chennai online grocery shop also outlined the real challenge of running an online grocery store is fulfilling customer’s need and wish. “We regularly do market survey in order to know consumer’s expectation and based on those surveys we discover in which particular product consumers are interested. Consequently, we alter our product line based on our surveys”. One of the interesting trends Subrmaniam has informed that sometimes he gets order from abroad to deliver the grocery for their respective family in Chennai. However, he reiterated about the fact that online grocery and FMCG products have great potential but it will take some time to flourish. III. Online sales promotions Online Sales Promotion is one of the important elements of marketing mix. There are so many elements of online promotion such as …  Advertising  Direct Marketing  Public Relations  Sales Promotion Online sales Promotions have been used by marketer to increase sales in the short term. Sales have grown in both importance and frequency over the past few decades. Although an accurate estimate for total online sales promotions expenditures does not exist, we can be sure that the trend is up.

Online Sales promotion serves three essential roles: It informs, persuades and reminds prospective customers about a company and its products. Even the most useful product or brand will be a failure if no one knows that it is available. As we know, channels of distribution take more time in creating awareness because a product has to pass through many hands between a producer and consumers. Therefore, a producer has to inform portal members as well as ultimate consumers about the attributes and availability of his products. The second purpose of online promotion is persuasion. The cut throat competition among different products puts tremendous pressure on their manufacturers and they are compelled to undertake sales promotion activities. The third purpose of online promotion is reminding consumers about products availability and its potential to satisfy their needs. From these elements Online Sales Promotion is the element which is in the focus of this study.

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A. Types of Online Sales promotions There are two types of Online Sales promotions I. Consumer Oriented Online Sales Promotion II. Trade Oriented Sales Promotion A.1. Consumer Oriented Online Sales Promotion Consumer Oriented Sales Promotion is the main topic of this article. Here emphasize is given to motivate consumer to increase sales. Consumer Oriented Online Sales Promotion includes Sampling, Couponing, Premiums, Contest, Refunds, Rebates, Bonus Pack’s, Price-off, Event marketing etc. Marketer’s uses consumer oriented sales promotion tools for the following reasons:  To increase short term sales  To induce trial  To reduce inventory  To establish a brand name  To make cross selling  To cope up with competition  To avoid advertising clutter Tools of Consumer Oriented Online Sales Promotion: There are so many tools or technique available to the marketers for achieving objective of sales promotion. These tools should be used considering all other factors affecting such as cost, time, competitors, availability of goods etc. These tools are as under… 1. 2. 3. 4. 5. 6. 7.

Coupons Price-Off Freebies Scratch Cards Lucky Draws Bundling Offer Extra Quantity

A coupon leads to price reductions so as to encourage price sensitive customers. Non users can try a product which may leads to regular sales. A reduction in price always increases sales but the use of this technique should be carefully considered in the current market situation. Price-off is the most preferred online sales promotion technique because consumers response very positively to this scheme. Freebies are a popular form of modern marketing and are some of the best things about the internet. The definition of freebies is products or services given away for free at no cost to the consumer. At different times, big and small companies often give away prizes and money which is too good to be true. Often it’s in the pursuit of more customers or a larger fan base and it often works. Factors Influencing Consumer Oriented Online sales promotion: Mainly four factors should be taken into account while determining the Online sales promotion program. 1. Target market 2. Nature of product 3. Stage of product life cycle 4. Budget available for promotion

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1. Target Market: While doing online sales promotion, marketer must know who their target market is; otherwise there is no use of all effort because it leads to nowhere. A target market can be in any of the stages of buying hierarchy i.e. awareness, knowledge, liking, preferences, conviction and purchase. Each stage defines a possible goal of promotion. 2. Nature of the product: There are various product attributes which influence sales promotional strategy. When the unit price is low the manufacturer as well as the customer has low risk but he can get the benefit of mass marketing. Therefore, mass marketing requires mass sales promotion schemes. Sales promotion scheme differ for products like its durability, perishable goods etc. 3. Stage of product Life Cycle: Online Sales promotion strategies are influenced by the life cycle of a product. When a new product introduced, prospective buyers must be informed about its existence and its benefits and middlemen must be convinced to stock it. Later, if a product becomes successful, competition intensifies and more emphasis is placed on sales promotion to increase its sales. 4. Budget Available for Promotion: The funds available for promotion are the ultimate determinant of the promotional programme. A business with ample funds can make more effective use of online sales promotion programme than a firm with limited financial resources. Online Sales Promotion from the Consumers point of view Willingness to buy on sales promotion offer Sixty per cent of the sample did not show willingness to buy a brand due to promotion while 30% showed willingness and 10% were not sure. This indicates that when 30% showed willingness and 10% consumers who were not sure, these groups might be lured through innovative and lucrative sales promotion offer. Ability to induce trial Forty per cent of the respondents had said that sales promotion had the ability to induce trial which reinforces the above inference. Long-term impact In order to understand ability of the promotions to increase long-term sales, respondents were asked about continuity of purchase of a brand after the withdrawal of promotion. Eighty per cent of the respondents indicated that they would not continue. But 20% said they would. Thus, it could be inferred that promotions in this category (low involvement products) might encourage trial and brand switching but not long term loyalty. Preference of Schemes: Price off was the most preferred type of scheme. Maximum customersâ&#x20AC;&#x2122; ranked price-offs as number one or two. Perceived Quality: Majority of respondents had a perception that the quality of the promoted brands remained the same during promotion, while some of them felt that it was inferior to before. It can be inferred that promotions were not leading to negative brand quality perceptions. It is found that some customer strongly preferred to buy their regular brand and said that sales promotion would not weaken their loyalty towards the brand. A.2. Trade Oriented Online Sales Promotion Trade Oriented Online Sales Promotion aimed to motivate channel member of the company and to encourage them to push companyâ&#x20AC;&#x2122;s product. Trade Oriented Online Sales Promotion includes dealer contest and incentives, trade allowances. Point-of-purchase displays, sales training programs, trade shows, cooperative advertising, and other programs designed to motivate distributors and retailers to carry a product and make an extra effort to push it to their customers

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Online Sales promotion from the retailerâ&#x20AC;&#x2122;s point of view: Perceptions on Scheme Preference It was found that retailer perceived price offs as a better form of sales promotion activity. Price offs in their opinion had relatively a greater impact compared to any other form of sales promotion activity like Bonus packs, Premium, Contests etc. Retailers preferred price offs the most, then bonus pack, premium, contests, in order of importance. Perceptions about Buying Roles Retailers viewed that the person who do online shopping, was the decider of a toilet soap brand and not the Income provider (e.g. head of the family). It could be inferred that visibility of information about the sales promotion activity at the point of purchase could result into the purchase of a promoted brand. Perceptions about Response to Online Sales Promotion Offers They believed that younger age-groups were more experimental in nature, amenable to trying new brands, and sought/looked at the time of purchase. Perceptions about Communications of Online Sales Promotion Schemes Retailers perceived that role of word of mouth and communication advertising played an important part in providing information inputs to consumers regarding online sales promotion activities. Dealer-Retailer Dynamics At the time of online sales promotion activities, dealers had tendency to push unwanted stocks onto the smaller retailers. In fact these retailers preferred to stock variety of brands and wanted payment for shelf and window display to increase traffic into their store. However, supermarkets and big retailers were pampered and given special services and given better margins and better allowances. Margins It was found that in online sales promotion schemes margins varied from 6 to15% depending of the size of the retail outlet, quantity ordered by him etc. Mostly margins were linked to size of the volumes that were ordered. Perceptions about terms and conditions Retailers were not found to be happy with online sales promotion schemes where their margins were cut on the pretext of just fast movement of inventory of the brand being promoted. Also if additional incentive was offered it was subject to minimum performance requirement. Problem of left-over A leftover stock at the end of any scheme was required to be sold by the retailers before they ordered fresh stocks. In case of bonus packs scheme, leftover stock was often dismantled (cut open buy one get one free) and sold them individually as a regular soap. This approach of the company leads to misappropriation which in turn could result in adverse brand image. Gifts for Retailer motivation Companies at times were rewarding retailers by giving free gifts like thermos flasks or clocks on purchase of above 1000/- or 5000/-, if they sold more than certain quantity in a given period. Companies were making a half-hearted effort to motivate retailers. Handling Problems Many a timeâ&#x20AC;&#x2122;s retailers had to handle various online sales promotion offers simultaneously in a category and also across categories and there was no formal communication planning either from the dealer or the company. Remembering each offer and handling was a problem especially for a small retailer which was often an as oneman show. IV. Methodology and Data analysis on Online Sales Promotions of Grocery and other FMCG products in Chennai Entity Consequently, this dissertation seeks to offer insight into how popular online promotions (price-discount, coupon and free shipping) influence consumer's quality perception and purchase intentions. Moreover, brand awareness was expected to moderate the relationship between promotion and consumer responses. To achieve this objective, a 3 (promotion: price-discount / coupon / free shipping) x 5 (brand: well-known / fictitious online retailers in Chennai) between-subjects factorial design experiment was conducted. The participants were 100 college students. The results revealed significant main effects for promotion and brand awareness on consumers'

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perceived quality. Specifically, in contrast with coupon promotion, price-discount revealed greater impact on consumer's perceived quality. In addition, well-known brand has successfully played a moderating role in the relationship between promotions and consumer responses. The data finding suggested that promotional strategies used by well-known brand/online retailers are more possible to result in more favorable responses. 1. Do you consider online promotional schemes while purchasing a particular brand of Grocery/any FMCG products? Particulars Yes No

Which of the following online promotional schemes you have come across so far? Promotional schemes Coupons price off Freebies scratch cards lucky draw Bundling extra qty.

Respondents 78 22

Respondents 56 80 25 21 9 30 66

Which medium do you feel is suitable to promote the various promotional schemes? Source Online TV Newspaper Radio Others

Respondents 80 59 43 15 12

A. Findings of the Data report: Online Sales Promotion, a short-term inducement, offered to a consumer or trade has gained momentum as a promotional tool world over. It represents nearly three fourth of the marketing budget at most consumer product companies. Online sales promotions can enhance consumers’ self-perception of being “smart” or a “good” shopper  FMCG are such a market where the level of loyalty remains low and this is because of many reasons.  Quality as the most influencing factors in the purchase decision while price is also an important for purchase decision.  Schemes always attract more and more consumers towards particular brand. Simultaneously it gives idea about the factors which consumers look most in the product before they make final decision.  Price off and extra quantity is the two main offers/schemes which consumers have came across at the time of purchase.  People are not much aware of the schemes which continue in the market it may be because of the present stock of the product at their place.  People are ready to switch over to another brand if they find better online promotional schemes which suits their budget means more good brand + less cost + quality.  Extra quantity with less or same price, more satisfaction, quality and other factors influence consumers to switch over to other brands.  People are more quality and price oriented.  Consumer remembers that name of the product by the company name and also from the past performance of that company.  Retailers are not suggesting purchasing particular brand because of personal relation or those customers are brand loyal.  Margin and of better relations with consumers and too provide quality product to consumers they suggest consumers too bye particular brand.  Customers are looking for any type of the promotions on the product before them going to purchase.  Price off, product bundling and extra quantity are more demanded by the consumers over others schemes.

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B. Disadvantages of Online promotions  Supply chain could be a great problem as for online operation it should be very imperative to have smooth network of supply chain.  In FMCG products and grocery retailers get very thin margin, consequently it would be tough for retailers to provide discount and offer on these products.  Customer satisfaction is a big challenge. Gaining and retaining customer’s faith is tough to maintain as there is no face to face interaction with consumers. V. Conclusion From the article it was found that the retailer would be a rich source of information about the consumer and the likely response to online sales promotion activities. Developing a system to tap such responses from time to time both at retailer and consumer level would be helpful for planning future sales promotion activities. In order to build trust and commitment online sales should tap preferences, perceptions of retailers as well as consumers. Thus, it is a easy process where we can view and get our favourite Grocery and other FMCG products like shampoo, soaps, snacks, cosmetics and even medicines near our door step within time is a boon for us. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

[15]

Adebanjo, D., 2000, “Identifying problems in forecasting consumer demand in the fast moving consumer goods sector”, Bench marking: An International Journal, 7, 3, 223-30. Asawanipont, N., 2003, “More Thais starting SMEs”, The Nation, 1 November. Beharrell, B., Dension, T.J., 1995, “Involvement in a routine food shopping context”, British Food Journal, 97, 4, 24-9. Blackburn, R., Stokes, D., 200, “Breaking down the barriers: using focus group to research small and medium-sized enterprises”, International Small Business Journal, 19, 1, 44-67. C.R.Kothari, “Research Methodology methods & techniques”, New Age international (p) ltd . Publishers, 2nd edition. ChangLiang Feng, Jing Wang, Xiao2yuan Huang, The Cost Model and its Optimization in Supply Chain, System Engineering, 2002, Vol. 20, No. 6, pp.12-13. Chaudhuri, A., 2000, “A macro analysis of the relationship of product involvement and information search: the role of risk”, Journal of Marketing Theory and Practices, 8, 1, 1-15. Cotterill, Ronald W., William P. Putsis Jr. and Ravi Dhar (2000), “Assessing the Competitive Interaction between Private Labels and National Brands”, Journal of Business: 109-137. Coulson, N.S., 2000, “An application of the stages of change model to consumer use of food labels”, British Food Journal, 102, 9,661-8. Cowley, J.C.P., 2000, “Strategic qualitative focus group research – define and articulate our skills or we will be replaced by others”, International Journal of Market Research, 42, 1, 17-39. Dholakia, M.U., 2001, “A motivational process model of product involvement and consumer risk perception”, European Journal of marketing, 35, 11/12, 1340-60. FaQuan Liu, ChengJie Zhi, Some improvements in gray forecasting model GM (1, 1), Mathematics in Practice and Theory, 2005, 35(11), pp.11-14. JuLong Deng, Grey system (social, economic), National Defense Industry Press, 1985. Kotler, P. and Keller, K. (2009). Marketing management (13 thed). Upper Saddle River, NJ: Prentice-Hall. Lawson, M., McGuinness, D., Esslemont, D. (1990). The Effect of In-store Sampling on the Sale of Food Products, Marketing Bulletin, 1990, 1, 1-6, Article 1. Philip Kotler, “Marketing Management”, 11th edition, Pearson education Asia Publication.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Research Perspective Review on Retinal Blood Vessel Detection Dr Ravi Subban, G. Padma Priya, P.Pasupathi, S.Muthukumar Dept. of Computer Science, School of Engineering and Tech., Pondicherry University, Pondicherry, India 3, 4 Centre for Information Technology and Engineering, M.S.University, Tirunelveli, India Dept. of Computer Science and Engineering, National Institute of Technology, Karailal, Pondicherry, India

1,2

Abstract. Retinal blood vessel detection is the emerging research field in digital image processing genre. It plays a vital role in medical imaging. In this paper, a review and study is made on human retinal blood vessel detection techniques in the research perspective. The most of the work focused in domain of medical industry. The identification and detection of this disease was carried out the commonly available techniques such as features extraction techniques, mathematical algorithms and artificial neural network classifiers. According to the performance and computational level, the analysis is made based on exactness of extraction of vessels. Keywords: Blood Vessel Detection; Local Entropy Thresholding; Matched Filter, Gaussian Mixutre I.

Introduction

Diabetic retinopathy is major cause for visual loss and visual impaired vision worldwide. A proper detection and treatment of this disease is needed in time. In the past few years, many approaches have been used for the identification and detection of this disease using some features extraction techniques, mathematical algorithms and artificial neural network classifiers which has some drawbacks in preprocessing, extraction of appropriate features, blood vessels extraction and in the selection of the classification techniques. The main cause of diabetic retinopathy (DR) is abnormal blood glucose level elevation, which damages vessel endothelium, thus increasing vessel permeability. The first manifestation of DR is tiny capillary dilations known as micro-aneurisms. DR propagation also causes neovascularization, hemorrhages, macular edema and in later stages of retinal detachment. Optic fundus has been widely used by the medical community for diagnosing vascular and nonvascular pathology. The inspection of the retinal vasculature may reveal hypertension, diabetes, cardiovascular disease, and stroke. Diabetic retinopathy is a major cause of blindness in adults due to changes in blood vessel structure and distribution such as new vessel growth (proliferative diabetic retinopathy) and requires laborious analysis from an ophthalmologist. The most effective treatment for many eye related diseases is the early detection through regular screenings. An automatic assessment for blood vessel anomalies of the optic fundus initially requires the segmentation of the vessels from the background. There are many previous works existing in segmenting blood vessels from retinal images. Figure 1 shows the general classification of the retinal blood vessel detection techniques. Figure 1. The general classification of the retinal blood vessel detection techniques. Retinal Blood Vessel Detection Technologies

Local Entropy Thresholding

Matched Filter Method

Gaussian Mixture Model Method

Others

The extraction of human retina is generally carried out using Local Entropy Thresholding, Matched filtered method, Gaussian mixture method and others method such as Gabor Wavelet, Hough transformation, Neural Network method, etc. A. Local Entropy Thresholding In a match-filtered retinal image, the enhanced blood vessels are usually very sparse and it is compared with the uniform background. This leads to a highly peaky co-occurrence matrix with low entropy that is not appropriate for local entropy thresholding. The local entropy thresholding method aims to maximize the local entropy of foreground and background without considering the unbalanced proportion between them. Therefore, the blood vessels extracted by the original local entropy thresholding method are usually not complete and some detailed structures are missed. Therefore two modifications are introduced to improve the results of blood vessel extraction that is essential to increase the performance of algorithm. First, the co-occurrence matrix definition is

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modified to increase the local entropy. The co-occurrence matrix of an image shows the intensity transitions between adjacent pixels. The original co-occurrence matrix is asymmetric by considering the horizontally right and vertically lower transitions. Here, some jittering effect is added to the co-occurrence matrix that tends to keep the similar spatial structure but with much less variations. Then by considering the sparse foreground, the optimal threshold is selected. The original threshold selection criterion aims to maximize the local entropy of foreground and background in a gray-scale image without considering the small proportion of foreground. Therefore, selecting the optimal threshold that maximizes the local entropy of the binarized image indicating the foreground/background ratio is determined. The larger the local entropy, the more will be the balanced ratio between foreground and background in the binary image [38]. A model based method was presented by K.A. Vermeera et al [6] for the retinal blood vessel detection obtaining a sensitivity of 92% with a specificity of 91%. The method can be optimized for the specific properties of the blood vessels in the image and it allows for detection of vessels that appear to be split due to specular reaction. Analysis of the result of the Laplace and threshold procedure resulted in an estimation of the distance between fragments (df ) of not more than four pixels. The objects were therefore dilated twice. A favorable solution to the detection of blood vessels was found by using the method proposed by Edgardo Felipe-Riveron and Noel Garcia-Guimeras [8]. To extract the skeleton of the resulting vascular network, morphological thinning and pruning algorithms were used. But the presence of noise can affect the detection in some degree and the false detection of the border of the optic disk is the other drawback. Adam Hooveret. al [5] have proposed a method for locating blood vessels in retinal images using threshold probing of a Matched Filter Response which segments roughly 75% of the vessels in a retinal fundus image with a false positive rate comparable to a human observer. The problem with this approach is that the property of connectedness is not compared in their evaluation. A novel local adaptive thresholding algorithm for detecting retinal blood vessel was proposed by Yongping Zhang et al. [11] using the nonlinear orthogonal projection to capture the features of vessel networks. The experimental results indicate that there is a common suited parameter for the procedure of nonlinear projection to all images used and more than 96 percent of pixels have been correctly classified for the images from the DRIVE and the STARE databases. Some methods suffers from the problems of heavy computation and manual intervention like the one proposed by Lili Xu and Shuqian Luo [17] for blood vessel detection in retinal images based on the adaptive local thresholding that produces a binary image to extract large connected components as large vessels. The residual image fragments in the binary image including some thin vessel segments (or pixels). The proposed algorithm is tested on DRIVE database, and the average sensitivity is over 77% while the average accuracy reaches 93.2%. Blood vessel segmentation method for high resolution retinal images was presented by J.Benadict Raja et al [23] using enhancement threshold which enhances the speed and accuracy of segmentation process. But the speed of segmentation is not completely satisfactory as it took 3.54 minutes for segmenting 3500x3000 size image. An automatic detection of multiple oriented blood vessels in retinal images using Gabor Filters and Entropic thresholding, which performs better with lower specificity even with the presence of lesions in the abnormal images [16]. Some of the very useful techniques used for the treatment of diabetic retinopathy patients in medical imaging department in detecting Macula in digital retinal images was proposed by Maryam Mubbashar [22] using Gabor Wavelet and Thresholding. A comparative study on retinal blood vessel detection was made by Monisha Chakraborty [29]. The algorithm comprises of four steps: second derivative Gaussian function filtering, local entropy thresholding, median filtering and length filtering. Thitiporn Chanwimaluang and Guoliang Fan [39] proposed an efficient blood vessel detection algorithm for retinal images using local entropy thresholding algorithm that retains the computational simplicity and achieves accurate segmentation results in the case of normal retinal images and images with obscure blood vessel appearance. In the case of abnormal retinal images with leisons, some lesions are also misdetected in addition to blood vessels. Manjunath V Gudur, Nanda [38] proposed an algorithm that is sensitive to lesions due to the fact that their boundaries partially match the shape of matched filter kernels. Even the smaller blood vessels can be extracted. B. Matched filter method A more robust approach to blood vessel detection is matched filter method [4]. This edge fitting based method used the concept of signal detection using matched filters to detect piecewise linear segments of blood vessels in retinal images. The cross section of a vessel in a retinal image was modeled by a Gaussian shaped curve. Blood vessels usually have poor local contrast. The two-dimensional matched filter kernel is designed to convolve with the original image in order to enhance the blood vessels. A prototype matched filter kernel is expressed as: (1) where L is the length of the segment for which the vessel is assumed to have a fixed orientation. Here, the direction of the vessel is assumed to be aligned along the y-axis. Because a vessel may be oriented at any angles, the kernel needs to be rotated for all possible angles. A set of twelve 15x16 pixel kernels is applied by convolving to a fundus image and at each pixel only the maximum of their responses is retained. Given a retinal IJEBEA 14-222; ÂŠ 2014, IJEBEA All Rights Reserved

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image, which has low contrast between blood vessels and background, its MFR version, where blood vessels are significantly enhanced. Two dimensional matched filter method [1] was used for detection of blood vessels in retinal images, which produces better results in analyzing fluoresce in angiogram images of the retina as well. With the minor modifications, this method could very well be extended to the extraction of the geographical features from satellite images and the enhancement of fingerprint images. The detection and quantification of retinopathy was done using digital algorithms based on filtering approach coupled with a priori knowledge about a retina [2]. It is an adaptive densitometric tracking technique based on local neighborhood information which can more accurately measure vessels widths under a broad range of diameters and clinical vessel geometries. But the centerline tracking algorithm may get confused and fail, because of the sample line location. Combination of the Matched Filter with a segmentation strategy by using a Cellular Automata method is used by Dalmau and Teresa Alarcon et al. [20]. The main problem with their method is in detecting thin vessels. Another difficulty is that the algorithm detects some lesions as vessels. Bob Zhang et al [18] proposed retinal vessel extraction method by matched filter with first-order derivative of Gaussian, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). It has much lower complexity and is much easier to implement. Blood vessels in each quadrant are extracted by running a mask and the area is calculated and thereby ISNT ratio is obtained by K.Narasimhan et al [25]. The performance of the proposed system can be increased by using a classifier. The experimental results shows that a sensitivity of 90% is obtained from the predefined set of images. C. Gaussian Mixture Model Method To extract the blood vessels from the fundus retinal image, the Expectation Maximization (EM) algorithm was adapted and applied to a Gaussian mixture distribution of the pixel intensities. The EM performs the segmentation by classifying vessel’s pixels in one class (foreground) and non-vessel’s pixels in the other (background). The EM output is obtained by iteratively performing two steps: E-step, which computes the expected value of the likelihood function (pixel class membership function) with respect to the unknown variables, under the expected parameters of a Gaussian mixture model and M-step which maximizes the likelihood function defined in the E-step until convergence. The EM algorithm takes the value of a pixel’s intensity as a random variable. Like other random variables, the pixel intensities of an image have a probability. Safia Shabbir et al [42] have presented a comparison and evaluation of computerized methods for blood vessel enhancement and segmentation in retinal images. First technique uses Gaussian filtering for preprocessing, LoG filtering for enhancement and adaptive thresholding for segmentation purpose. Second technique uses unsharp masking for preprocessing, Gabor wavelet for enhancement and global thresholding for segmentation. An automated detection and grading of diabetic maculopathy in digital retinal images was made by Anam Tariq et al [44] using Gaussian Mixture Model-based classifier with the low accuracy of 94.37 % as compared to 97.38 % of GMM. Usman Akram et al proposed a method for the identification and classification of micro aneurysms for early detection of diabetic retinopathy. A Gaussian mixture model based system for detection of macula in fundus images was proposed by Anam Tariq et al [35]. The results have shown the significance of the proposed system and are proved to be competitive when compared with other results in the literature. Usman Akram [27] proposed an algorithm for the detection of neovascularization for screening of proliferative diabetic retinopathy with 96.35%, 98.93% and 98.37% of sensitivity, specificity and PPV respectively. Segmentation of retinal blood vessels using Gaussian mixture models and expectation maximization was tried by DjibrilKaba et al [40] for matched filter response image using the Expectation Maximization algorithm. It has an advantage over other tracking-based methods because it applies a two-dimensional matched filter on retinal images to enhance vessel appearance and allows multiple branches models. D. Fuzzy C-means (FCM) Clustering Algorithm The fuzzy C-means (FCM) clustering algorithm can be used for retinal blood vessel segmentation using graylevel and moment invariants-based features [34]. Jegatha R et al used SVMs classifiers that provided robust and computationally efficient blood vessel segmentation method while suppressing the backgrounds. The majority of errors were due to background noise and non-uniform illumination across the retinal images and the border of the optic disc. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. Both qualitative and quantitative experiments on normal and abnormal retinal images indicate that the proposed approach is effective and can produce identical results as the ground truth and yield a higher accuracy ratio and a lower misclassification ratio than the manual extractions [10]. Automated detection of dark and bright lesions in retinal images can be used for early detection of diabetic retinopathy [28] [30]. E. Gabor Wavelet Transform Method 2-D Gabor Wavelet and sharpening filter can be used to enhance and sharpen the vascular pattern for preprocessing and blood vessel segmentation of retinal images performing well in preprocessing, enhancing and IJEBEA 14-222; © 2014, IJEBEA All Rights Reserved

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segmenting the retinal image and vascular pattern [12]. Computer aided diagnostic system for grading of diabetic retinopathy was presented by Anam Tariq et al [43]. Although the proposed system focused only on reliable detection of abnormalities, but still the system can be used for automatic screening of diabetic retinopathy. Personal identification system based on vascular pattern of human retina was proposed by Sana Qambery et al [33]. F. Transformation Method A.S. Semashko et al [24] used Hough transform for optic disk segmentation using blood vessels location information which outperforms many other methods and has a high rate of very good segmentations. G. Neural Network Method Diego Marín et al [15] have proposed a new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. The total time required to process a single image is less than approximately one minute and thirty seconds but this performance might still be improved. Chanjira Sinthanayothin et al [3] have proposed an automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images [25]. Blood vessels were identified by means of a multilayer perceptron neural network. The sensitivity and specificity of the recognition of each retinal main component was as follows: 99.1% and 99.1% for the optic disc; 83.3% and 91.0% for blood vessels; 80.4% and 99.1% for the fovea. Artificial neural networks have been extensively investigated for segmenting retinal features such as the vasculature was presented by Edward James, Antonio Francisco [36]. As supervised methods are designed based on pre-classified data, their performance is usually better than that of unsupervised ones and can produce very good results for healthy retinal images. M. S. Sidhu et al [19] used Femtosecond (fs) laser microsurgery using neural network method. With that successfully captured and analyzed retinal blood vessel images from intact porcine eyes. This work has a potential application for progress in detecting and accurately monitoring retinal pathologies and in their surgical treatment. H. Dijkstra’s Shortest Path Algorithm Rolando Estrada et al [21] have presented an exploratory Dijkstra forest based automatic vessel segmentation with an applications in video indirect ophthalmoscopy. This method is more likely to label a pixel as vascular if it can be directly connected to a large vascular region than if it is isolated, since the latter case is more indicative of noise rather than an actual vessel. I. Combined Cross-point Number (CCN) Method A.M. Aibinu et al [13] have proposed vascular intersection detection in retina fundus images using a new hybrid approach, which is able to detect the vascular bifurcation and intersection points in fundus images. A ROCbased analysis for this algorithm gives a very high precision and true positive rate with a very small false error rate. J. Water Flow-Based Method Xin U Liu, Mark S Nixon [9] proposed water flow based vessel detection in retinal images, which has been assessed on synthetic and real images showing excellent detection performance and ability to handle noise. K. Centerlines and Morphological Bit Plane Slicing Method A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images producing 0.932 to 0.965 for accuracy, 0.66 to 0.83 for true positive rate, 0.01 to 0.06 for false positive rate and 0.73 to 0.89 for precision rate [31]. L. Two Dimensional Medialness Function Method Elahe Moghimirad et al [32] presented a method for segmenting retinal blood vessel based on a weighted twodimensional (2D) medialness function. A multi-scale method to segment retinal vessels based on a weighted two-dimensional (2D) medialness function. Supervised methods were presented by Ana Maria Mendonça and Aurélio Campilho [7] for the segmentation of blood vessels in ocular fundus images. The processing time of the algorithm is less than 2.5 min for an image of the DRIVE database and less than 3 min for a STARE image. M. Heuristic Function Ahmed Hamza Asad et al [37] have proposed an improved ant colony system for retinal blood vessel segmentation by applying a new heuristic function obtaining sensitivity from 0.6472 to 0.8188, specificity from 0.8883 to 0.9213 and accuracy from 0.8670 to 0.9122. N. Curvelet Transform and Morphological Operators Shirin Hajeb Mohammad Alipour et al [41] used digital curvelet transform (DCUT) and morphological operations. Experiments show that the system achieves respectively the specificity and sensitivity of (>98% and IJEBEA 14-222; © 2014, IJEBEA All Rights Reserved

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>96%) for normal stage, (>98%, >95%) for Mild/Moderate Non-Proliferative DR (NPDR), and (>97% and >93%) for Sever NPDR+PDR. O. Final Segmentation Mask Method Ibaa Jamal et al [26] proposed a novel method with final segmentation mask prepaid by combining fine background segmentation mask and fine noise segmentation mask. The results are confirmed by visual inspection of segmented images taken from the standard diabetic retinopathy databases. P. Noise Segmentation Mask Method Anam Tariq and M. Usman Akram [14] used binary noise segmentation mask which includes the noisy area and it is applied on retinal image. Retinal image segmentation is done on the colored retinal images by extracting background and noise effect from the image. II.

Discussion

A study and analysis on the segmentation of blood vessel in retinal images using the different methods proposed by the researchers is made. The efficient algorithms used for fully automated blood vessel segmentation in ocular fundus images have been studied. Most of the algorithms performs very well in extracting blood vessels. Even the smaller blood vessels can be extracted. Matched filtering enhances the contrast of blood vessels against the background. Modified local entropy thresholding algorithm takes into account the spatial distribution of gray levels, performing efficiently in distinguishing between enhanced vessel segments and the background since it can preserve the structure details of an image. However, the presence of lesions in the abnormal retinal image is the major obstacle in extracting blood vessels since they are also misenhanced and misdetected as blood vessels. The algorithms are sensitive to lesions due to the fact that their boundaries partially match the shape of matched filter kernels. Improvement in the robustness of the algorithms can be done by involving color information and additional anatomical constraints. It is concluded that the method proposed by Chanjira Sinthanayothin et al [3] have identified the blood vessels in retina image by means of a multilayer perceptron neural network. The sensitivity and specificity of the recognition of each retinal main component was as follows: 99.1% and 99.1% for the optic disc; 83.3% and 91.0% for blood vessels; 80.4% and 99.1% for the fovea. III.

Conclusion

In this paper, survey has been made in the retinal blood vessel detection techniques, which is based on the positive rate, accuracy, extraction method and qualitative aspect of the detection. These kinds of works will help to step into the further enhancement in this domain, to attain the accurate, fastest and smartest devices. Research in this paper will adhere to the field of medicine sciences. IV. References [1] [2] [3]

[4] [5] [6] [7]

[8] [9] [10] [11] [12] [13]

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B., & Ravichandran, C. G. (2011). Blood Vessel Segmentation For High Resolution Retinal Images, 8(6), 389–393. [24] Semashko, A. S., Krylov, A. S., & Rodin, A. S. (2011). Using Blood Vessels Location Information in, 384–393. [25] Narasimhan, K., Vijayarekha, K., Joginarayana, K. A., Sivaprasad, P., &Satishkumar, V. (2012). Glaucoma Detection From Fundus Image Using Opencv. [26] Jamal, I., Akram, M. U., & Tariq, A. (2012). Retinal Image Preprocessing : Background and Noise Segmentation, 10(3), 537–544. [27] Akram, M. U., Tariq, A., & Khan, S. A. (2012). Detection of Neovascularization for Screening of Proliferative Diabetic Retinopathy, 372–379. [28] Akram, U. M., & Khan, S. a. (2012). Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy. Journal of Medical Systems, 36(5), 3151–62. doi:10.1007/s10916-011-9802-2 [29] Chakraborty, M. (2012). Comparative Study on Retinal Blood Vessel Detection, 2(6), 1292–1301. [30] Dey, N., Roy, A. B., Pal, M., Das, A., & Bengal, W. (2012). FCM Based Blood Vessel, 1(3), 1–5. [31] Fraz, M. M., Barman, S. a, Remagnino, P., Hoppe, a, Basit, a, Uyyanonvara, B.Owen, C. G. (2012). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer Methods and Programs in Biomedicine, 108(2), 600–16. doi:10.1016/j.cmpb.2011.08.009 [32] Moghimirad, E., Hamid Rezatofighi, S., &Soltanian-Zadeh, H. (2012). Retinal vessel segmentation using a multi-scale medialness function. Computers in Biology and Medicine, 42(1), 50–60. doi:10.1016/j.compbiomed.2011.10.008 [33] Qamber, S., Waheed, Z., & Akram, M. U. (2012). Personal identification system based on vascular pattern of human retina. 2012 Cairo International Biomedical Engineering Conference (CIBEC), 64–67. doi:10.1109/CIBEC.2012.6473297 [34] Science, M. E. C. (2012). Retinal Blood Vessel Segmentation using Gray-Level and Moment Invariants-Based Features. [35] Tariq, A., Shaukat, A., & Khan, S. A. (2012).A Gaussian Mixture Model Based System for Detection of Macula in Fundus Images, 33–40. [36] James, E., & Francisco, A. (2013). On Supervised Methods for Segmentation of Blood Vessels in Ocular Fundus Images, 1(1), 21–37. [37] Asad, A. H., Azar, A. T., Fouad, M. M. M., &Hassanien, A. E. (2013). An Improved Ant Colony System for Retinal Blood Vessel Segmentation, 199–205. [38] Gudur, M. V, & Nanda, S. (2013). Automatic Retinal Vessel Segmentation Using Modified Local Entropy Thresholding Algorithm, 137–141. [39] Thitiporn Chanwimaluang and Guoliang Fan (2013). An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding. School of Electrical and Computer Engineering Oklahoma State University, Stillwater. [40] Kaba, D., Salazar-Gonzalez, A. G., Li, Y., Liu, X., &Serag, A. (2013). Segmentation of Retinal Blood Vessels Using Gaussian Mixture Models and Expectation Maximisation, 105–112 [41] Hajeb, S., Alipour, M., & Rabbani, H. (2013). A New Combined Method Based on Curvelet Transform and Morphological Operators for Automatic Detection of Foveal Avascular Zone A New Combined Method Based on Curvelet Transform and Morphological Operators for Automatic Detection of Foveal Avascular Zone Abstract. [42] Shabbir, S., Tariq, A., & Akram, M. U. (2013). A Comparison and Evaluation of Computerized Methods for Blood Vessel Enhancement and Segmentation in Retinal Images. International Journal of Future Computer and Communication, 2(6), 600–603. doi:10.7763/IJFCC.2013.V2.235 [43] Tariq, A., Akram, M. U., & Javed, M. Y. (2013). Computer aided diagnostic system for grading of diabetic retinopathy. 2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), 30–35. doi:10.1109/CIMI.2013.6583854 [44] Tariq, A., Akram, M. U., Shaukat, A., & Khan, S. a. (2013). Automated detection and grading of diabetic maculopathy in digital retinal images. Journal of Digital Imaging, 26(4), 803–12. doi:10.1007/s10278-012-9549-4

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Parametric Optimization of SAW Welding Parameters using Taguchi L9 Array Mr. Pradeep Deshmukh1, 2Prof. S. N. Teli Department of Mechanical Engineering Saraswati College of Engineering, Kharghar Navi Mumbai India Abstract: Submerged Arc Welding is one of the major welding processes in industry because of its inherent advantages, including deep penetration and a smooth bead. Lots of critical sets of input parameters are involved in Submerged Arc Welding Process which needs to be controlled to get the required weld bead quality Submerged arc welding (SAW) process is an important component in many industrial operations. The research on controlling metal transfer modes in SAW process is essential to high quality welding procedures. Quality has now become an important issue in today’s manufacturing world. Experiments are conducted using submerged arc process parameters viz. welding current, arc voltage and welding speed (Trolley speed) on mild steel of 12 mm thickness, to study the effect of these parameters on penetration depth. The experiments are designed using Taguchi method (with Taguchi L9 orthogonal array) considering three factors and three levels. Keywords: Regression Analysis, Welding Parameters, Submerged Arc Welding, Taguchi Method. I.

Introduction

Submerged arc welding (SAW) is widely used welding process in most fabrication industries. It requires a noncontinuously fed consumable solid or tubular (flux cored) electrode. The molten weld and the arc zone are protected from atmospheric contamination by being submerged under a blanket of granular fusible flux consisting of lime, silica, manganese oxide, calcium fluoride, and other compounds. When molten, the flux becomes conductive, and provides a current path between the electrode and the work. This thick layer of flux completely covers the molten metal thus preventing spatter and sparks as well as suppressing the intense ultraviolet radiation and fumes that are part of the shielded metal arc welding (SMAW) process. In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, welding speed; arc voltage and electrode sickouts since they are closely related to the geometry of weld bead, a relationship which is thought to be complicated because of the non-linear characteristics. However, trialand-error methods to determine optimal conditions incur considerable time and cost. In order to overcome these problems, non-traditional methods have been suggested. Bead-on-plate welds were carried out on mild steel plates using semi-automatic SAW machine. Data were collected as per Taguchi’s Design of Experiments and analysis of variance (ANOVA) was carried to establish input–output relationships of the process. In this study, the process parameters affecting weld quality in SAW have been identified and their effects on performance measures have been analysed using an inexpensive and easy-to-operate experimental strategy based on Taguchi’s parameter design. Further, an attempt has been made to analyse the impact of more than one parameter on welding in the hard facing process because the resultant performance output is the combined effect of the impacts of several interacting parameters in actual practice. The experimental strategy has been adapted from the methodology outlined for successful parametric appraisal in other applications. K. Srinivasulu Reddy [1], in his paper presented optimization & prediction of welding parameters and bead geometry in submerged arc welding. He collected data as per Taguchi’s Design of Experiments and analysis of variance (ANOVA) and experiment was carried to establish input–output relationships of the process. By this relationship, an attempt was made to minimize weld bead width, a good indicator of bead geometry, using optimization procedures based on the ANN models to determine optimal weld parameters. The optimized values obtained from these techniques were compared with experimental results and presented. Vukojevic, N., Oruc, M., Vukojevic, D. et al.[2,3], done performance analysis of substitution of applied materials using fracture mechanics parameters. Younise, B., Rakin, M., Medjo, B., et al. performed numerical analysis of constraint effect on ductile tearing in strength mismatched welded CCT specimens using micromechanical approach. Sharma, A., Chaudhary, A. K., Arora, N., et al.[4],done estimation of heat source model parameters for twinwire submerged Arc welding[4]. Pillia, K. R., Ghosh [5,6] have presented some investigations on the Interactions of the Process Parameters of Submerged Arc Welding. Reducing of trial run is essential to reduce the cost of welding procedure. Ghosh, A., Chattopadhyaya [7], presented prediction of weld bead penetration, transient temperature distribution & haz width of submerged arc welded structural steel plates for the submerged arc welding plates, engineers often face the problem of selecting appropriate combination of input

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process control variables for achieving the required weld bead quality or predicting the weld bead quality for the proposed process control values. Juang and Tarng [8] have adopted a modified Taguchi method to analyze the effect of each welding process parameter (arc gap, flow rate, welding current and speed) on the weld pool geometry (front and back height, front and back width) and then to determine the TIG welding process parameters combination associated with the optimal weld pool geometry. Lee et al. [9] have used the Taguchi method and regression analysis in order to optimize Nd-YAG laser welding parameters (nozzle type, rotating speed, title angle, focal position, pumping voltage, pulse frequency and pulse width) to seal an iodine-125 radioisotope seed into a titanium capsule. II.

Taguchi Method

Taguchi’s philosophy is an efficient tool for the design of high quality manufacturing system. Dr. Genichi Taguchi, a Japanese quality management consultant, has developed a method based on orthogonal array experiments, which provides much- reduced variance for the experiment with optimum setting of process control parameters. Orthogonal array (OA) provides a set of well- balanced (minimum experimental runs) experiments and Taguchi’s signal-to-noise ratios (S/N), which is logarithmic functions of desired output serve as objective functions for optimization. This technique helps in data analysis and prediction of optimum results. In order to evaluate optimal parameter settings, Taguchi method uses a statistical measure of performance called signal-tonoise ratio. The S/N ratio takes both the mean and the variability into account. The S/N ratio is the ratio of the mean (signal) to the standard deviation (noise). The standard S/N ratios generally used are as follows: Nominal is best (NB), lower the better (LB) and higher the better (HB). The optimal setting is the parameter combination, which has the highest S/N ratio. Essentially, traditional experimental design procedures are very complicated and not easy to use. A large number of experimental works have to be carried out when the number of process parameters increases. To solve this problem, the Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with only a small number of experiments. The utmost advantage of this method is the saving of effort in conducting experiments; saving experimental time, reducing the cost, and determining significant factors quickly. Step 1: Identification of important process variables. Step 2: Development of process plan. Step 3: Conducting experiments as per the plan. Step 4: Recording the responses. Step 5: Testing the welded job. Step 6: Finding out the optimized values of the parameters Step 7: Presenting the main and substantial effects of process parameters. III.

Experimentation: Taguchi Method

The Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with only a small number of experiments. The greatest advantage of this method is the saving of effort in conducting experiments; saving experimental time, reducing the cost, and discovering significant factors quickly. Levels of process parameters identified are shown in Table 1. The experiment was conducted at the Welding Centre of “TOPWORTH PIPES & TUBES LTD”, Mumbai with the following set up. MODEL-LINCON1000,automatic SAW equipment with a constant voltage, rectifier type power source with a 1000A capacity was used to join the two mild steel pipes of size12000mm (length) X 1250 mm (width) X 12 mm (thickness) with a V angle of 30 o to 45o ,4 mm root height and 0.75 mm gap between the two plates. Copper coated Electrode Auto melt EH-14 wire size: 3.20 mm diameter, of coil form and basic fluoride type granular flux were used.Text Font of Entire Document. Table I: Welding parameters with different levels Symbol

Welding Parameters

Level 1

Level 2

Level 3

Welding current(amp)

500

600

700

Arc voltage(volts)

Welding speed (mm/min)

450

550

650

Electrode stick out(mm)

The loss function of the higher the better quality characteristic can be expressed as:

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Where, yi are the observed data (or quality characteristics) at the ith trial, and n is the number of trials at the same level. The overall loss function is further transformed into the signal to noise ratio. In the Taguchi method, the S/N ratio is used to determine the deviation of the quality characteristic from the desired value. The S/N ratio (η) can be expressed as: η Regardless of the quality of the quality characteristic, a large S/N ratio corresponds to a better quality characteristic. Therefore, the optimal level of the process parameters is the level with the highest S/N ratio as shown in Table 5. L9 3 Level Taguchi Orthogonal Array Taguchi’s orthogonal design uses a special set of predefined arrays called orthogonal arrays (OAs) to design the plan of experiment. These standard arrays stipulate the way of full information of all the factors that affects the process performance (process responses). The corresponding OA is selected from the set of predefined OAs according to the number of factors and their levels that will be used in the experiment. Below Table No.2 shows L9 Orthogonal array. Table II: Experimental layout using L9 orthogonal array Trial No.

Welding Current

Arc Voltage

Welding Speed

Electrode Stick Out

Multiple Regression Analysis Multiple regression analysis technique is used to ascertain the relationships among variables. The most frequently used method among social scientists is that of linear equations. The multiple linear regression take the following form: Where Y is the dependent variable, which is to be predicted;X 1,X2,X3 . . . . . . . .X k are the known variables on which the predictions are to be made and a, b1, b2, b3,……….bk are the coefficient, the values of which are determined by the method of least squares. Multiple regression analysis is used to determine the relationship between the dependent variables of bead width and weld bead hardness with welding current, arc voltage, welding speed, and electrode stick out. The regression analysis was done by Minitab 15 version. After completion of the welding process the welded specimen has been kept properly on a table and the weld bead width has measured with the help of a measuring scale. Similarly S/N ratio for weld bead width has been found separately. The largest signal to noise ratio (mean) is considered to be the optimum level, as a high value of signal to noise ratio indicates that the signal is much higher than the random effects of the noise factors. Table 3 shows the mean S/N ratios for the welding current, arc voltage, welding speed and electrode stick out. From the Table 3, it is evident that largest signal to noise ratio (average) is the optimum level, because a high value of signal to noise ratio indicates the signal is much higher than the random effects of the noise factors. The largest S/Navg for parameter is indicated by Optimum in the Table 5. Results shown in table 5, it can be stated that contribution of current is maximum and contribution of speed and electrode stick out are minimum in optimum bead width. IV.

Result And Discussion

This paper has presented the application of Taguchi technique to determine the optimal process parameters for SAW process. Experimentation was done according to the Taguchi’s design of experiments. Using the signal-

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to-noise ratio technique the influence of each welding parameters are studied and the prediction of the bead geometry is done. Then it is used to predict the SAW process parameters for any given welding conditions. From the available 9 data sets, 9 data sets are used to train the network as given in Table 3. Table III: Training data sets Trial No.

Arc Voltage (Volts) 31

Welding Speed mm/min

Welding Current (Amps) 500

Bead Width Measured (mm)

450

Electrode Stick Out (mm) 28

500

550

500

650

600

550

600

650

600

450

700

650

700

550

700

450

Table IV: Experimental layout using L9 orthogonal array and S/N ratio for weld bead width Arc Volt age (Volts)

Weld ing Speed mm/min

Elec trode Stick Out (mm)

Weld ing Curr ent (Amps) 500

Mean square deviati on

S/N ratio (dB)

Bead Width Measu red (mm) 14

450

196

22.92

500

550

196

22.92

500

650

225

23.52

600

550

256

24.08

600

650

256

24.08

600

450

289

24.60

700

650

256

24.08

700

550

289

24.60

700

450

289

24.60

Trial No.

Table V: Mean S/N ratio for weld bead width Weld Parameters

Levels

Welding Current

Level 1(500) ----------------Level 2 (600) --------------Level 3(700)

Mean S/N ratio 23.12 ------24.25 -----24.42

Arc Voltage

Level 1(31) ---------------Level 2 (32) --------------Level 3(33) Level 1(450) ---------------Level 2 (550) -----------------Level 3(650) Level 1(28) ------------Level 2 (30) --------------Level 3(32)

23.69 -----23.86 ------24.24 24.04 ------23.86 ------23.89 24.04 -----23.89 ------23.86

Welding Speed

Electrode Stick Out

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From Table 3 it can be predicted that the optimum level parameters for achieving optimum result of weld bead width if the path A2-B3-C1-D1 is followed: [Welding current (A2) 600A, Arc voltage (B3) 32V, Welding speed (C1) 450 mm/min, electrode stick out (D1) 28 mm]. Multiple regression analysis has been used to determine the relationship between the dependent variables of bead width with welding current, arc voltage, welding speed, and electrode stick out. The regression analysis of the input parameters is expressed in linear equation as follows: Predicted Weld bead width 13.7-0.125A+1.13B-0.375C+0.375D ………… (4) =13.7-0.125 x welding current + 1.13 x Arc voltage -0.375 x welding speed + 0.375 x Electrode stick out. From the above equations, predicted values of weld bead width has been found out and tabulated with the measured value at Table 4. V.

Conclusion

An experiments was carried out to establish the relationship between process variables and optimization tools are used to find an optimal solution. It is observed that the developed of model is a powerful tool in experimental welding optimization, even when experimenter does not have to model the process. A Taguchi orthogonal array, the signal to noise (S/N) ratio and analysis of variance were used for the optimization of welding parameters. A confirmation experiment was also conducted and verified the effectiveness of Taguchi optimization method. A. Confirmation test for weld bead width A test sample, having same size and dimension as per earlier specification has been taken and performed welding at the optimum predicted process parameters at path, welding current, 600A, Arc voltage 32 V, Welding speed 450mm/min and Electrode stick out 28 mm. VI. [1] [2] [3] [4] [5] [6] [7] [8] [9]

References

K. Srinivasulu Reddy, “Optimization & Prediction of Welding Parameters and Bead Geometry in Submerged Arc Welding”, International Journal of Applied Engineering Research and development (IJAERD) ISSN 2250-1584 Vol. 3, Issue 3, Aug 2013. Vukojevic, N., Oruc, M., Vukojevic, D. et al., "Performance analysis of substitution of applied materials using fracturemechanics parameters", Tehnicki vjesnik-Technical Gazette, 17, 3 (2011), pp. 333-340. Younise, B., Rakin, M., Medjo, B., et al.,"Numerical analysis of constraint effect on ductile tearing in strength mismatched welded CCT specimens using micromechanical approach", Tehnicki vjesnik- Technical Gazette, 17, 4(2010), pp. 411- 418. Sharma, A., Chaudhary, A. K., Arora, N., et al.,"Estimation of heat source model parameters for twinwire submerged Arc welding", International Journal of Advanced Manufacturing Technology, 45, 11-12(2009), pp. 1096-1103. Pillia, K. R., Ghosh, A., Chattopadhyaya, S., Sarkar, P. K., Mukherjee, K.,"Some investigations on the Interactions of the Process Parameters of Submerged Arc Welding", Manufacturing Technology & Research (an International Journal), 3, 1(2007), pp. 57-67. Ghosh, A., Chattopadhyaya, S., Sarkar, P. K.,"Output Response Prediction of SAW Process", Manufacturing Technology & Research (an International Journal), 4, 3(2008), pp. 97-104. Ghosh, A., Chattopadhyaya, S.,"Prediction of Weld Bead Penetration, Transient Temperature Distribution & HAZ width of Submerged Arc Welded Structural Steel Plates", Defect and Diffusion Forum, 316-317(2011), pp. 135-152. S. C. Juang and Y. S. Tarng, Process parameters selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel, J. of Materials Processing Technology, Vol. 122, 2002, 33-37. H. K. Lee, H. S. Han, K. J. Son and S. B. Hong, Optimization of Nd-YAG laser welding parameters for sealing small titanium tube ends, J. of Materials Science and Engineering, Vol. A415, 2006, pp. 149-155.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Database Designing for the Online Examination Application Using SAPABAP Preeti Singh Bhadoria#1, Kapil Nimker#2, Sanjay Ojha#3 School of Management, Centre for Development of Advanced Computing (CDAC), Noida ____________________________________________________________________________________________________

Abstract: The Online Examination Application [1] is an application which utilizes SAP as a platform and is developed using ABAP language. The Database of this application is thus responsible for holding the various data right from the time of registration of the candidates to the final module of displaying the results all is performed using the database tables. This paper thus deals with the different tables as utilized by the application and hence helps in bringing out the effectiveness of the database in any similar application. Keywords: SAP, ERP, OEA, DATA TYPES, DATABASE TABLES. __________________________________________________________________________________________ I. Introduction A database is essentially an electronic filing system that houses a collection of information organized in such a way that allows a computer program to quickly find desired pieces of data [2]. In the simplest form, a database is composed of tables, columns (called fields), and rows (called records or data). A classic example of a database is a telephone book, which is organized alphabetically so as to make it possible to quickly find a desired piece of data. The telephone book can be considered a table, a storage container for information. Within this table is typically found three columns (or fields)窶馬ame, address, and telephone number. Within each of these fields exists rows (or records), the simplest form of data in the database. The basic structure of a database is quite similar to a Microsoft Excel spreadsheet wherein columns (fields) store row after row of records (data). The biggest difference between a database and a spreadsheet is simply that databases can contain multiple tables that are connected to one another through relationships. Thus, a database can be thought of as a much more complex, and ultimately much more useful, spreadsheet. The database plays a key role in each SAP system, as it houses all the data that are used by that particular SAP component or product. SAP can use a variety of different brand name database releases ranging from very expensive and imminently flexible to very inexpensive and yet quite capable. II. The various data types as used in ABAP Like every other programming language ABAP also has some predefined data types with defined ranges which are used with the different variables that come across while composing a program. The data types in ABAP can be broadly categorized in the following way [4]: A. Elementary types: Elementary types are part of the dual-level domain concept for fields in the ABAP Dictionary. The elementary type has semantic attributes, such as texts, value tables, and documentation, and has a data type. There are two different ways to specify a data type: By directly assigning an ABAP Dictionary type: We can assign a predefined ABAP Dictionary type and a number of characters to an elementary type. The ABAP Dictionary has considerably more predefined types than the ABAP programming language. The number of characters here is not the field length in bytes, but the number of valid characters excluding formatting characters. The data types are different because the predefined data types in the ABAP Dictionary have to be compatible with the external data types of the database tables supported by the SAP Web AS ABAP. When we refer to data types from the ABAP Dictionary in an ABAP program, the predefined Dictionary types are converted to ABAP types as follows: Figure 1 Predefined dictionary data type in ABAP Dictionary type DEC INT1 INT2 INT4 CURR CUKY QUAN UNIT PREC FLTP NUMC CHAR

Meaning Calculation/amount field Single-byte integer Two-byte integer Four-byte integer Currency field Currency key Quantity Unit Accuracy Floating point number Numeric text Character

Maximum length n 1-31, 1-17 in tables 3 5 10 1-17 5 1-17 2-3 16 16 1-255 1-255

ABAP type P((n+1)/2) Internal only Internal only I P((n+1)/2) C(5) P((n+1)/2) C(n) Internal only F(8) N(n) C(n)

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LCHR Long character 256-max C(n) STRING String of variable length 1-max STRING RAWSTRING Byte sequence of variable length 1-max XSTRING DATS Date 8 D ACCP Accounting period YYYYMM 6 N(6) TIMS Time HHMMSS 6 T RAW Byte sequence 1-255 X(n) LRAW Long byte sequence 256-max X(n) CLNT Client 3 C(3) LANG Language internal 1, external 2 C(1)

Assigning a domain: The technical attributes are inherited from a domain. Domains are standalone Repository objects in the ABAP Dictionary. They can specify the technical attributes of a data element. One domain can be used by any number of data elements. When you create a domain, you must specify a Dictionary data type (see above table) and the number of characters. B. Reference types: Reference types describe single fields that can contain references to global classes and interfaces from the ABAP class library. In an ABAP program, we can use the TYPE addition to refer directly to a data element. The predefined Dictionary data types of the domain are then converted into the corresponding ABAP types. If we define a local data type in a program by referring to a data element as follows: TYPES dtype TYPE data_element then the semantic attributes of the data element are inherited and will be used, for example, when we display a data object with type dtype on the screen. Since all data types in the ABAP Dictionary are based on data elements, they all contain the corresponding semantic attributes. C. Structures: A structure is a sequence of any other data types from the ABAP Dictionary, that is, data elements, structures, table types, or database tables. When we create a structure in the ABAP Dictionary, each component must have a name and a data type. In an ABAP program, we can use the TYPE addition to refer directly to a structure. If you define a local data type in a program by referring to a structure as follows: TYPES dtype TYPE structure. III. Types of database tables The Database in this application is implemented is implemented by means of tables. These tables can be grouped into different types based on their usage and requirement. Hence, the different tables are as following [2]: A. Transparent tables: A transparent table is a table that stores data directly. Transparent table is a one to one relation table i.e. when a transparent table is created then exactly same table will be created in data base and it is basically used to store transaction data. The transparent table contains a single table. For each transparent table there is one associated table in the database. The database table has the same name, same number of fields and the fields have the same names. A single table can have one or more primary keys as well as secondary indexes can also be created. They can be accessed using open and native SQL. These tables are used to hold master data. B. Pool tables: Pooled tables are logical tables that must be assigned to a table pool when they are defined. Pooled tables are used to store control data. Several pooled tables can be combined in a table pool. The data of these pooled tables are then sorted in a common table in the database. They are used to hold a large number of very small tables (stores customizing data or system data).It has a many-to-one relationship with a table in the database. It is stored with other pooled tables in a single table called table pool in the database. The database table has different name, different number of fields and fields have different names. Table pools contain more tables than table clusters. Primary key of each table does not begin with same fields or fields. However, secondary indexes cannot be created. They can be accessed using open SQL only. They reduce the amount of database resources needed when many small tables have to be opened at the same time. C. Cluster tables: Cluster tables are logical tables that must be assigned to a table cluster when they are defined. Cluster tables can be used to store control data. They can also be used to store temporary data or texts, such as documentation. They are used to hold data from a few large tables. It has a many-to-one relationship with table in the database. Many cluster tables are stored in a single table in the database called a table cluster. The database table has different name, different number of fields and fields have different names. These tables contain lesser tables than table pools. Primary key of each table begins with same fields or fields but the secondary indexes cannot be created. These tables can be accessed using open SQL only. They would be used when the tables have primary key in common and data in these tables are all accesses simultaneously.

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Preeti Singh Bhadoria et al., International Journal of Engineering, Business and Enterprise Applications, 8(1), March-May., 2014, pp. 48-52

IV. Database tables in online examination application The database of the Online Examination Application is basically composed of three main tables apart from the ones that are automatically created by the system. These tables are required to hold the information of the registration details of the candidates, show the various questions that can be uploaded along with the possible options and finally the third table is of the results which holds the correct options as well as computes the total of the candidates. The different tables thus are as following: A. The registration table: The Registration Table is the first table named as ZOES_REGTABLE is responsible for holding the various entries which are to be made by the candidate through the registration page in order to register him to take up the test. The candidate is required to make the entries like the email id, his name, address and the contact number. It is mandatory on the part of the candidate that all the entries are made i.e. the candidate cannot skip any of the entries otherwise the registration is cancelled. The Registration Table of the Online Examination Application can be seen as following: Figure 1: The Registration Table

Data types of the registration table: In the database tables it is required that the entries have to be specified with a particularly specified data type. Here also the data types of all the entries have been mentioned. The email, names and address fields have been mentioned of the type character. The date of birth has been mentioned of type DATS. The pincode and the contact number have been mentioned of type number. It is interesting to note that there are also various type of validation checks in the different fields. The email field prevents the use of the different special characters, spaces and the use of the capital letters. The names field also prevents the use of the special characters and the numerals. The fields that have been declared of type number have a validation of inserting only numerals and all other data types have been restricted. Primary key for the registration table Primary key is the key which is required to be unique and should always be not null. In the registration table the email id of the candidate has been declared as the primary key. This is because the email id of each candidate is always a unique entity which is the foremost requirement for any entry to be declared as the primary key. The contents of the Registration Table can be seen as following where the different entries are shown: Figure 2: Entries held in registration table.

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Preeti Singh Bhadoria et al., International Journal of Engineering, Business and Enterprise Applications, 8(1), March-May., 2014, pp. 48-52

B. The question upload table: The Question Upload Table is named as the ZOESEXAM. This table is responsible for holding the various questions along with the four possible options. The last option shows the correct answer for that question. The ZOESEXAM table can be seen as below with the various table entries: Figure 3: The Question Upload Table

Data types of the question upload table: This table consists of the question number which is defined of type number. The next field entry is the question number which is of type string. Followed by the question are the four options for each question which are again of type character. Lastly there is option which happens to be the correct answer for that particular question which of type character. Primary key for the question upload table: The primary key for this table is the question number which is unique entity for each table. Hence, as it serves the uniqueness feature of the primary key therefore, it is chosen as the same. The figure below displays the contents of the question upload (ZOESEXAM): Figure 4: Entries held in Question Upload Table

C. The result table: This table is the final of the three tables created for the Online Examination Application. This table is responsible for holding the answers to the questions as have been answered by the various candidates. This table is especially important as it helps in computation of the result of each candidate by comparing his answers with the correct ones. If the answer happens to be correct than the marks are allotted for the same. However, if the answer is incorrect than as per the marking scheme the negative marks are deducted from the total and likewise the result is generated. This table is mentioned as ZOES_RESULT. The table with its various entries is shown in the figure 5. Data types of the result table: The result table consists of four data entry fields. The first entry i.e. the question number is of type number. The next table entry is the user_name which is the name of the candidate and is defined of type character. The next field is k_date which signifies the date on which the exam was taken by the candidateis of type DATS. The last table entry is the Response field which holds the option that has been selected by the candidate as his reply to the question. Primary key for the result table: The primary key for the ZOSE_RESULT table is actually a combination of the three fields i.e. the question number, user name (user_name) and the date (k_date) on which the exam was taken. These here are combined by the use of a join operation and then taken as a single primary key for the entire table.

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Preeti Singh Bhadoria et al., International Journal of Engineering, Business and Enterprise Applications, 8(1), March-May., 2014, pp. 48-52

Figure 5: The Result Table

This was done as taking any of the keys singly would not help to maintain the uniqueness criteria for the primary key. The question number field is not chosen since the same question as will be answered by the different candidates will tend to over write the previous question by the next one. Secondly, the user_name field could not be taken as the primary key as there can possibly be two or more than two candidates by the same name. finally, the third field i.e. The exam date (k_date) is not chosen as the primary key as, if the test is again intended to be taken at some future date then it will over write all the previous test details and hence the primary requirement of data consistency does not stand accomplished. Hence, owing to these reasons the above mentioned keys have not been mentioned as the primary key instead all the three have been combined together to form a single and unique primary key. The table ZOES_RESULT with the contents can be seen as in the figure below. The figure shows the different questions along with their correct answers and the ones that have been answered by the candidates. Upon matching the answers with their correct options the marks of the individual candidate are also computed which can then be sent to the candidate via email. Figure 6: Entries held in The Result Table

V. Conclusion Hence, it can be concluded that using the different database tables not only adds to the usability of the system but also makes it much more convenient to handle. Here also we see that the different tables that have been created serve the purpose of handling the data effectively and thus they reduce the ambiguity of the data. VI.

References

[1].Kapil, Shwetank Sharma, Sanjay Ojha , “Online Examination Application using SAP ABAP”, Unpublished. [2].http://www.informit.com/articles/article.aspx?p=425384 [3].http://www.saptechnical.com/Tutorials/ABAP/Pool/Index.htm [4].http://help.sap.com/saphelp_nw70/helpdata/en/fc/eb3138358411d1829f0000e829fbfe/content.htm

VII. Acknowledgements It is a well-known fact that no task is achieved single handedly. It is always a collective task that is rewarded. Hence, in the present task too there are a number of people who are responsible in bringing out this paper in its current form. We would first of all like to extend our gratitude the entire management at CDAC Noida for supporting and providing us with the opportunity to utilize such a platform. We would also like to thank Ms. Mary Jacintha (HOD, School of Management) and all the respective faculty members for their constant support and guidance.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net INSIGHTS INTO AWARENESS LEVEL AND INVESTMENT BEHAVIOUR OF SALARIED INDIVIDUALS TOWARDS FINANCIAL PRODUCTS Puneet Bhushan Assistant Professor, Department of Humanities and Social Sciences, Jaypee University of Information Technology, Waknaghat, Solan, India. __________________________________________________________________________________________ Abstract: Diverse financial products have been introduced these days in Indian market. Each of these financial products offer a range of benefits and varying options with respect to interest rates, exposure to risk, time period of the contract, fees etc. Most of the individuals are not able to take advantage of higher returns offered by these products due to lack of financial awareness. Thus they must be made aware about risk and return characteristics of these products by designing an appropriate financial education program so that people can invest in these financial products. For designing an effective financial education program, current awareness level as well as investment behavior of individuals towards financial products must be known. This paper examines the awareness level and investment behavior of salaried individuals towards financial products. Results of the study suggest that respondents are quite aware about traditional and safe financial products whereas awareness level of new age financial products among the population is low. Also majority of the respondents park their money in traditional and safe investment avenues. Keywords: Financial products, financial literacy, investment preferences, awareness level. __________________________________________________________________________________________ I. INTRODUCTION These days a plethora of new age financial products are available in the market. Each of these financial products offer a range of benefits and varying options with respect to interest rates, exposure to risk, time period of the contract, fees etc. Due to this increased complexity of financial products and services, individuals find it difficult and cumbersome to take financial decisions. In order to understand the features and characteristics of these products, an individual must be financially literate. Financially literate individuals can make effective use of these financial products and services by evaluating associated risks and returns and finally choosing those products which are best suited to them. India has one of the highest savings rate in the world which shows that Indians are having a high propensity to save. But most of the savings made by Indian households is in the form of bank deposits, thus the allocation of savings is a great cause for concern. Reserve Bank of India in its report has mentioned that only 1.4% of Indian households’ savings was invested in equity, mutual funds and debentures in 2003-2004. Another survey (Shukla, 2009) has found that over 80% of Indians save but 36% of the Indian households keep their savings at home. 51% households keep their savings in bank deposits whereas stock and insurance accounted for only 3% of estimated household savings in 2007-2008, which is very less. Indians by investing their money in low yielding instruments and traditional financial products are not able to take advantage of new age financial products which have the potential to generate higher returns. The main reason behind this is low financial literacy (Bhushan and Medury, 2013). It is very important that investors should become more financially aware so that our country of savers can get converted into a country of investors. For the development of financial markets, it is important that in addition to safe and traditional financial products available in the market, people also invest in new and innovative financial products which have the potential to provide higher returns to investors. It is also important from the viewpoint of financial service providers to gain an insight into awareness level and investment preferences of individuals so that accordingly financial products can be developed. For improving financial literacy of individual’s there lies a need for financial education. For designing an effective financial education program, current awareness level as well as investment behavior of individuals towards financial products must be known. This paper is an attempt to examine the awareness level of salaried individuals about various financial products as well as to find out the investment preference of salaried individuals towards various financial products. II. LITERATURE REVIEW A number of research studies have been undertaken in India and abroad to identify the investment behaviour of retail investors and households. Gupta et al. (2001) studied the Indian household investors’ preferences, future intentions and experiences and found that bonds were regarded as an investment for the retired people but that did not have much appeal for young people. The market penetration achieved by mutual funds was found to be

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much lower than equity shares for all age classes. Gupta and Jain (2008) on the basis of an all-India survey of 1463 households found the preferences of investors among the major categories of financial assets, such as investment in shares, indirect investment through various types of mutual fund schemes, other investment types such as exchange-traded gold fund, bank fixed deposits and government savings schemes. The study provides interesting information about how the investorsâ&#x20AC;&#x2122; attitude towards various investment types are related to their income and age, their portfolio diversification practices, and the over-all quality of market regulation as viewed by the investors themselves. Verma (2008) studied the effect of demographics and personality on investment choice among Indian investors and found that mutual funds were popular amongst professionals, students and the self employed. Retirees displayed their risk aversion by not investing in mutual funds and equity shares. It was also found that higher the education, higher was the level of understanding of investment complexities. Graduates and above in qualification preferred to invest in equity shares as well as mutual funds. Davar and Gill (2009) investigated the underlying dimensions in the selection of different investment avenues for the households. The results of the study revealed emphasis on familiarity, satisfaction, opinion and demographic dimensions for all investment avenues. Geetha and Ramesh (2011) studied the Indianâ&#x20AC;&#x2122;s behaviour about investment preferences. The data for their study was collected with the help of questionnaire and their total sample size was 210 respondents. They studied the effect of demographic variables on investment preferences. The study found that people were not aware about all the investment options available to them and they lack knowledge about securities. Girdhari and Satya (2011) analysed the investment preferences of individuals in urban Orrisa (one of the states in India). Through their study they found that investment decisions and risk tolerance of investors depends on age, sex, income, marital status, education, family background and occupation. It was also found that male investors are more risk seekers as compared to female investors. Das (2011) analysed the preferred investment avenues of the households in Nagaon district of Assam. The findings of the study reveal that insurance products are the most preferred investment avenues of the households. For carrying out this study, a structured questionnaire was used. Education levels, awareness about the financial system, age of investors were found to be the significant factors while making investment decisions. Income level was also found to be the important factor which influences investment decisions. The results of the study shows that higher income group shows relatively high preference towards investment in share market, conversely lower and average income group shows keen preference towards insurance and banks as the most preferred investment avenues. Samudra and Burghate (2012) studied the investment behavior of the middle class households in Nagpur. Bank deposits were found to be the most popular instrument of investment followed by insurance. Small savings scheme such as PPF, Post office savings deposits are the third preferred investment option. Amongst the factors which influence the decision to invest in a particular instrument it was found that return from the investment ranks first. Chaturvedi and Khare (2012) examined the investment pattern and awareness of the Indian investors about different investment instruments. The results suggest that age, education, occupation and income level of the individual affects their investment behaviour. Awareness of respondents towards traditional investment options is much higher than that for corporate securities, mutual funds, equity shares and preference shares. They also identified the factors which contribute to investor awareness. They found that occupation, education and income level affects the awareness level of investors towards various investment avenues. Jain and Mandot (2012) studied the impact of demographic factors on investment decisions of investors in Rajasthan, India. The questionnaire was developed for this study and total sample of 200 investors from different cities of Rajasthan has been taken for the purpose of the study. The study concludes that various demographic factors like age, marital status, gender, city, income level, market knowledge, occupation and educational qualifications have major impact on investment decision of investors in Rajasthan. Also it was found that gender and city have no impact on investment decision of investors. Sood and Medury (2012) analysed the investment preferences of working adults in Delhi, Gurgaon and Noida. The results of their study showed that investment preferences are not affected by age, gender, income, marital status and employment status. Bashir et al (2013) studied the investmnent preferences and risk level of salaried individuals in Gujrat and Sialkot provinces of Pakistan. The results of the study suggests that females are more risk averse than males whereas young and educated people are attracted more towards new risky investment opportunities and want to invest money in these instruments but they hesitate because of limited resources and lack of investment opportunities as well as absence of investment rends. Bhushan and Medury (2013) analysed the gender differences in investment behavior of employees working in various universities of Himachal Pradesh. India. They found that employees working in various universities of Himachal Pradesh invest in almost all investment avenues available to them. There is an overall inclination of investing in safe investment instruments. Gender differences in investment preferences are significant for health insurance, fixed deposits and market investments. After studying the literature on financial products it can be concluded that in India awareness towards traditional investment options is much higher than for corporate securities, mutual funds, equity shares and preference shares. Also the investment preference of individuals is more towards bank deposits and insurance products.

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1. 2.

III. OBJECTIVES OF THE STUDY To examine the awareness level of salaried individuals about various financial products. To find out the investment preference of salaried individuals towards various financial products.

IV. METHODOLOGY For this study, Himachal Pradesh (a state in India) is taken as an area of study. All those salaried individuals of Himachal Pradesh whether in government or non-government job were considered as the population for this study. Primary data from the respondents was collected by using a structured questionnaire. Multistage sampling has been adopted for collection of the data. There are total of twelve districts in Himachal Pradesh. Out of these three districts namely Shimla, Solan and Kangra were selected randomly (first stage). The selected districts are further divided on the basis of sub-divisions. In each of the selected districts further 2 sub-divisions were selected randomly (second stage). From each selected sub-division, the required number of salaried individuals was selected based on purposive sampling by using some criteria like place of work, occupational status and the attitude of the respondents to cooperate for the study, so as to get the representative sample of the population. Total sample of 516 respondents were used for the purpose of this study. V. ANALYSIS AND DISCUSSION Awareness regarding financial products In order to find out the awareness level of respondents towards various financial products available in the market, responses were measured on a 5 point Likert scale, assigning 1 to ‘very low’ awareness level and 5 to ‘very high’ awareness level. From the collected responses weighted mean for each of the financial product was calculated . Based on the mean score, financial products were ranked giving 1 st rank to the financial product whose mean score was highest and 14th rank to that product whose score was the least (Table 1). Results indicate that highest awareness is for bank fixed deposits followed by savings account, life insurance, post office savings, public provident fund, national savings certificate, kisan vikas patra, pension funds, mutual funds, stock market, bonds, debentures, commodity market and forex market. Thus it can be clearly seen that respondents are quite aware about traditional and safe financial products whereas awareness level of new age financial products among the population is low. This points out to the fact that due to low awareness level people are not able to take advantage of various financial products available in the market. Chaturvedi and Khare (2012) also found in their study on Indian investors that awareness of respondents towards traditional investment options is much higher than that for corporate securities, mutual funds, equity shares and preference shares. Thus in order to strengthen our financial system, people must be made aware about the characteristics of new age financial products. They must be made aware about risk and return characteristics of these products so that people can invest in these financial products. This will strengthen our financial system. People will also get the opportunity to invest in different financial products thereby increasing their chances of earning more returns from their investments. Table 1: Awareness of Financial Products

Percent

Count

Very High Percent

High Count

Percent

Count

Neutral Percent

Low Count

Percent

Count

Very Low

Mean

Rank

Savings Account

0.6

1.6

16.9

318

61.6

100

19.4

3.98

Bank Fixed Deposits

0.2

2.3

13.6

309

59.9

124

24.0

4.05

Public Provident Fund

2.5

8.9

196

38.0

177

34.3

16.3

3.53

Kisan Vikas Patra

3.3

111

21.5

196

38.0

135

26.2

11.0

3.20

National Savings Certificate

5.0

100

19.4

182

35.3

126

24.4

15.9

3.27

Post Office Savings

1.7

10.7

182

35.3

143

27.7

127

24.6

3.63

Pension Funds

4.8

156

30.2

142

27.5

110

21.3

16.1

3.14

Mutual Funds

10.1

109

21.1

193

37.4

124

24.0

7.4

2.97

Debentures

18.2

234

45.3

129

25.0

8.7

2.7

2.32

Bonds

17.2

209

40.5

135

26.2

13.0

3.1

2.44

Life insurance

2.5

5.0

18.2

291

56.4

17.8

3.82

Stock market

18.0

186

36.0

123

23.8

19.0

3.1

2.53

Commodity Market

238

46.1

190

36.8

8.1

7.0

1.9

1.82

Forex market

280

54.3

162

31.4

7.2

6.2

1.0

1.68

Source: Primary Data

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Investment preferences Respondents have been asked to provide information about all those financial products where they have invested their money. From their responses, it can be said that almost 95% of the respondents invest their money in bank fixed deposits followed by 77.7% of respondents who invest in life insurance. 59.3% of respondents invest in post office savings and almost 52.9% respondents invest in mutual funds (Table 2). Only 1.2 respondents invest in commodity market and no respondent invests in forex market. The results are in line with Samudra and Burghate (2012). In their study on investment behavior of the middle class households in Nagpur, bank deposits were found to be the most popular investment instrument followed by insurance. Das (2011) found that insurance products are the most preferred investment avenues of the households. Table 2: Investment in Financial Products Financial Products Frequency Percent of cases 490 95.0 Bank Deposits 306 59.3 Post Office Savings 273 52.9 Mutual Funds 159 30.8 Stock Market 11 2.1 Debentures 401 77.7 Life Insurance 202 39.1 Public Provident Fund 127 24.6 Pension Funds 52 10.1 Bonds 6 1.2 Commodity Market 516 Total Respondents Source: Primary Data From the analysis of the results it is clear that majority of the respondents park their money in traditional and safe investment avenues. More people invest in mutual funds as compared to stock market. Results also indicate that very few people invest their money in debentures as well as commodity market. Investment behaviour of respondents investing in traditional investment avenues can be attributed to the fact that they are not aware about the characteristics of new financial products, thereby loosing good investment opportunities. Results also point out to the fact that only 24.6% respondents have invested in pension funds, which means most of the people do not plan for retirement which is not a very healthy sign. Also 77.7% people have invested in life insurance which means that people are aware about the importance of life insurance. Results also indicate that only 39.1% respondents invest in public provident fund. This means that despite being a very good investment option, only few people invest in public provident fund, this can be due to longer lock in period of public provident fund and low level of awareness associated with public provident fund. VI. CONCLUSIONS Respondents are quite aware about traditional and safe financial products whereas awareness level of new age financial products among the population is low. Majority of the respondents park their money in traditional and safe investment avenues. Overall results suggest that people must be made more aware about new investment opportunities available in the market. They must be properly educated about new financial products available in the market, so that they can get advantage of earning higher returns. Moreover they will not get cheated by sales personnel as they will have knowledge regarding the charges levied by a company selling financial products and they will invest in financial products only after weighing risk return characteristics of the financial products. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

Bashir, T., Ahmed, H. R., Jahangir, S., Zaigam, S., Saeed, H., & Shafi, S. (2013). Investment preferences and risk level: Behaviour of salaried individuals. IOSR Journal of Business and Management, 10(1), 68-78. Bhushan, P., & Medury, Y. (2013). Gender differences in investment behaviour among employees. Asian Journal of Research in Business Economics and Management. 3(12), 147-157. Bhushan, P., & Medury, Y. (2013). Financial literacy and its determinants. International Journal of Engineering, Business and Enterprise Applications, 4(2), 155-160. Chaturvedi, M., & Khare, S. (2012). Study of saving pattern and investment preferences of individual household in India. International Journal of Research in Commerce and Management, 3(5), 115-120. Das, S.K. (2011). An Empirical analysis on preferred investment avenues among rural and semi-urban households. Journal of Frontline Research in Arts and Science, 1, 26-36. Davar, Y.P., & Gill, S. (2009). Antecedents of households’ investment decision-making process: A study of the Indian households. South Asian Journal of Management, 16(4), 44-75. Geetha, N., & Ramesh, M. (2011). A study of people’s preferences in investment behaviour, IJEMR, 1(6), 1-10. Girdhari, M., & Sathya, S.D. (2011). A study on investment preferences among urban investors in Orrisa. Prerna – Journal of Management Thoughts and Practices, 3(1), 1-9.

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[9] [10] [11] [12] [13] [14] [15]

Gupta, L. C., Gupta, C.P., &Jain, N. (2001). Indian Households’ Investment Preferences: A Survey. Society of Capital Market Research and Development. Alankrit Printers, New Delhi. Gupta, L.C., & Jain, N. (2008). Indian Households’ Investment Preferences: The Third All India investors’ Survey. Conducted by Society for Capital Market Research and Development. Jain, D., & Mandot, N. (2012). Impact of demographic factors on investment decision of investors in Rajasthan. Researchers World – Journal of Arts, Science and Commerce, 3(2), 81-92. Samudra, A., & Burghate, M.A. (2012). A Study on investment behaviour of middle class households in Nagpur. International Journal of Social Sciences and Interdisciplinary Research, 1(5), 43-54. Shukla, R. (2009). How India Earns, Spends and Saves. Study conducted by National Council of Applied Economic Research (NCAER) and Max New York Life Insurance Company Ltd. Sood, P. B., & Medury, Y. (2012). Investment preferences of salaried individuals towards financial products. International Journal of Management and Behavioural Sciences, 1(1), 95-107. Verma, M. (2008).Wealth management and behavioral finance: the effect of demographics and personality on investment choice among Indian investors. The Icfai University Journal of Behavioral Finance, 5(4), 31-57.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Implementing security to OFDM symbols of 802.11n networks 1

SANTOSH, 2VINOD B DURDI Department of Telecommunication Engineering, Dayananda Sagar College of Engineering, VTU, Bangalore, India 1, 2

Abstract: This paper mainly deals with the steganographic channels in high speed 802.11n networks. Here the modification of cyclic prefix in OFDM has been discussed. This is the highest hidden transmission technique known till date. Here the main focus is on the theoretical analysis and simulation results of the steganographic system performance.

Keywords: OFDM, network Steganography, IEEE 802.11n I. INTRODUCTION Since there is a great demand for fast and reliable wireless transmission there is a need to extend the standardization limit of WLAN standards. In the existing solution the main focus is on the security and offered throughput. The former has been solved by the IEEE 802.11i extension, significant “acceleration” was given after a few years with the approval of the standard IEEE 802.11n. 802.11 [1] based networks protect users’ privacy with advanced cryptographic solutions. However, that user is still vulnerable to steganographic systems that could be implemented in their wireless network. Fast 802.11n networks are, therefore, potentially a great hidden transmissions carrier. The main objective of this paper is to analyze the cyclic prefix information hiding techique which is based on OFDM modulation [2] specifically for IEEE 802.11n networks. In order to implement this system modified model of the 802.11n physical layer is used. The system requirements are MATLAB 7.10, Microsoft Windows 7. II. EXISTING SYSTEM Steganography for 802.11 was proposed by Szczypiorski according to him information has been hidden in the intentionally corrupted packets HICCUPS. Also as discussed in [3] and [4] the information has been hidden in the intentionally corrupted packets where secret information will be in the modified fields of the frame headers.WiPad, were information is hiding in the padding [5]. Disadvantages of existing methods: i. The existing system is not fast as the cyclic prefix steganographic system system. ii. Existing system is having more cost. III. OFDM IN 802.11N OFDM (Orthogonal Frequency-Division Multiplexing) means simultaneous transmission of independent data streams in single radio channel. In OFDM subcarriers will be orthogonal to each other. In FDM resources are shared according to the available bandwidth to a radio channels. In each of these channels the data streams of each user are transmitted. In the radio environment there is problem of multipath propagation where the receiver receives not only the signal propagated but also the delayed copies of that signal which is the main reason for ISI (Inter symbol Interference). In order to reduce ISI [6], in OFDM modulation, a special guard interval (GI) is inserted between symbols as shown in Fig.1. The IEEE 802.11 standards implement the guard interval by copying the ending part of each OFDM symbol and adding it in front of that symbol. The total single symbol transmission time (Tsym) is then the sum of the useful part of the symbol (Tu) and the duration of the GI as shown in Fig.1.

Fig.1 Cyclic prefix generation In the case of OFDM modulation in IEEE 802.11 networks, the GI is assumed to be a constant value TCP = 0.8μs. Optionally, 802.11n allows the shortening of the GI duration to TCP = 0.4μs in the case of good propagation conditions.

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Santosh et al., International Journal of Engineering, Business and Enterprise Applications, 8(1), March-May., 2014, pp. 58-61

IV. DISCUSSION The cyclic prefix technique is based on the existence of the cyclic prefix in the OFDM modulation. It improves the quality of transmission in the standard network. The cyclic prefix has to be introduced at the right place ie before its transmission and after the modulation. The duration of the GI remains unchanged the described modification does not affect the correct operation of the wireless network. The IEEE 802.11n standard distinguishes several modulation and coding schemes (MCSs) and allows the usage of one from the four modulations (BPSK, QPSK, 16-QAM or 64-QAM). It is chosen in such a way as to obtain the maximum possible bandwidth in a given environment at a given moment. The cyclic prefix technique modifies the cyclic prefix at physical layer. There is a problem of choosing antennas and the transmitters whose prefixes are to be modified. The need to finding and read these modified symbols in the steganographic receiver [7] must be taken into account. In order to accomplish this transmitter and receiver should have identical pseudo random number generators (PRNGs). This type of generator allows the creation of a sequence of numbers that is similar to random. The most important feature is that such a sequence is created in the deterministic manner that is based on the input source. In other words, in the case of two identical generators with the same input, both output random sequences are the same. In order to increase the security of this system both the transmitter and the receiver should use secret private key as the input to their generators [8]. Randomly generated numbers are from 0 to Tmax and determine the distance between symbols. Scheduled to have modified cyclic prefixes this allows system dependency on a secret that is known only to the hidden transmission parties. The synchronization between the transmitter and the receiver is the time of the pseudo-random number sequence and the beginning of the hidden transmission. For this purpose, the transmitter must notify the steganographic receiver of setting-up the hidden channel and the message length [9] (number of fragments). After receiving the starting information, both parties generate a random sequence of the desired length and gradually, with an appropriate spacing between modified symbols (prefixes), send fragments of the secret message. V. SYSTEM PARAMETERS In case of BPSK modulation the code rate is 1/2, for a single transmitting antenna of 20 MHz [10] channels. Here if the GI TCP = 0,8 μs (i.e. Tsym = 4 μs) there are symbol rate = 250,000 OFDM symbols per second transmitted. Each symbol carries 52 coded bits (NCBPS) and half of them are data bits (NDBPS). From the above example in BPSK modulation, the useful part of the OFDM symbol (Tu = 3.2 μs) carries 52 bits. Therefore, as in 0.8 μs of the GI it is possible to carry 13 bits, in the case of the modification of each OFDM symbol and avoiding using error correction coding, it is possible to achieve 3.25 Mb/s capacity of the hidden channel. The capacity can be determined from below mentioned equation[10]. CMAX = NCBPS . (TCP / Tu ) . SR

(1)

Based on the modulation used the maximum capacity is [10]: 3.25 Mb/s when using BPSK modulation. 6.5 Mb/s when using QPSK modulation. 13.0 Mb/s when using 16-QAM modulation. 19.5 Mb/s when using 64-QAM modulation. In the case of ordinary network users the steganography system user includes an additional cost which results from the interference of the hidden system implemented in the network. In the cyclic prefix technique user don’t lose their available bandwidth since the secret information is carried in parts of the OFDM system which are ignored anyway. Security is accomplished by the randomized selection of modified symbols that is based on a secret key. Detection (without knowing the key) of the steganographic channel created in the cyclic prefix technique requires observation and study of the cyclic prefixes in every single OFDM symbol in the network. Moreover, such an unauthorized observer should be able to separate and compare the two extremes of the already modulated OFDM symbol. For the casual user, who does not feel the presence of a hidden channel, this task is impossible, especially when the secret message is additionally encrypted. VI. SIMULATION RESULTS From Fig.2. It shows that, while using 64-QAM, with SNR = -10 dB and Tmax = 0, gained throughput is greater than 10Mb/s. However, in this case, the BER is too high to read the entire message properly.

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Fig. 2 BER as a function of SNR in the hidden channel for the AWGN radio channel model

From Fig.3. We can say that BER as a function of SNR for chosen MCS in the ordinary channel with and without the steganographic system implemented will generate same result. which means the stegenographic system hidden transmission uses part of the OFDM symbol which will not uses extra bandwidth which will not demands extra cost. Fig. 3 BER as a function of SNR with steganographic system implemented.

VI. CONCLUSION The cyclic prefix steganographic system does not generate any additional cost that ordinary users would incur. The secret private keys, introduced in the system generate random spaces between transmitted hidden message fragments which increases security of the system. In this system there is a need to set an additional signaling channel that would be used to agree the value of private keys and provide information about the start of transmission. V. References [1] [2] [3] [4]

[5] [6] [7]

Cho Y., Kim J., Yang W., Kang C., “MIMO-OFDM Wireless Communications with MATLAB”, John Wiley & Sons Ltd., 1996. Anibal Louis Intini, “OFDM multipliexing for wireless networks”, University of California. Frikha L., Trabelsi Z., El-Hajj W., “Implementation of a Covert Channel in the 802.11 Header”, Proc. Wireless Communications and Mobile Computing Conference (IWCMC 08), 6–8 Aug. 2003, pp. 594–599, doi: 10.1109/IWCMC.2003.103. Szczypiorski K., “A Performance Analysis of HICCUPS – a Steganographic System for WLAN”, Proc. International Conference on Multimedia Information Networking and Security (MINES 2010), –20 Nov. 2010, vol. 1, pp. 569–572, doi: 10.1109/MINES.2010.248. 28–54, First Quarter 2009, doi: 10.1109/MCAS.2009.915504. Szczypiorski K., “HICCUPS: Hidden Communication System for Corrupted Networks”, Proc. 10th International MultiConference on Advanced Computer Systems (ACS’2003), 22–24 Oct. 2010, pp. 31–40. IEEE Standard 802.11n-2008, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications – Amendment Krätzer C., Dittman J., Lang A., Kühne T.,“WLAN Steganography: A First Practical Review”, Proc. 8th Workshop on Multimedia and Security, 26–27 Sept. 2009, pp. 17–22,doi: 10.1145/1161366.1161371.

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[8] [9] [10]

Paul T., Ogunfunmi T., “Wireless LAN Comes of Age: Understanding the IEEE 802.11n Amandment”, Circuits and Systems Magazine, IEEE, vol. 8, no. 1, pp. Title “Fundamentals of wireless communication”. David Tse. Publisher. Cambridge university press 2005. Szymon Grabski, Krzysztof Szczypiorski. “Steganography in OFDM Symbols of Fast IEEE 802.11n Networks”, IEEE 2013.

Author profile 1

Santosh, received B.E Degree in Electronics and Communication from Visvesvaraya Technological University in 2011, Currently Pursuing M.Tech Degree in Digital Communication and Networking from Visvesvaraya Technological University in 2014, Department of Telecommunication Engineering, Dayanand Sagar College of Engineering, Bangalore India. 2

Vinod B Durdi received BE (Electronics and Communication) from Karnataka University, Dharwad, in 2000. He obtained M.Tech (Digital Communication) from Visvesvaraya Technological University (VTU), Belgaum, in 2003. He is presently pursuing Ph.D form Visvesvaraya Technological University (VTU), Belgaum, India. His research interest includes video processing, wireless networks, network security and chaos communication. He has authored and coauthored the various research papers in major national and international conference proceeding. He is currently Associate Professor in Dayananda Sagar College of Engineering, Banglore, India.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net To study the role of manufacturing competency in the performance of Sonalika tractor manufacturing unit Chandan deep Singh1, Palwinder Singh2, Jaimal Singh Khamba3 Assistant Professor, Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India 2 Research Scholar, Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India 3 Professor, Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India

Abstract: This study deals with the manufacturing competency of Sonalika tractor manufacturing unit. During the entire survey different factors have been analyzed like Product design and development, Quality control and Performance Parameters and different inferences have been concluded. It has been analyzed that the joint venture affects the sales because through the joint venture the maximum investment can be done in the Manufacturing plant & precise testing has been possible which automatically increase the testing level & production level as well. It has also been concluded that sales have improved with an improvement in competitiveness of manufacturing unit. Keywords: Competency, manufacturing unit, quality control, production time. I.

Introduction

A competency is the capability to apply a set of related knowledge, skills, and abilities to successfully perform functions or tasks in a defined work setting. Competencies often serve as the basis for skill standards that specify the level of knowledge, skills, and abilities needed for success, as well as potential measurement criteria for assessing competency attainment. A core competency is a fundamental understanding, capability, or proficiency in a specific subject area or skill set. For example, an individual who becomes licensed as a Microsoft Certified Software Engineer (MCSE) is said to have a core competency in certain Microsoft systems and networks. Companies with definite strengths in the marketplace, such as data storage or the development of accounting applications, can be said to have a core competency in that area. The core part of the term indicates that the individual has a strong basis from which to gain the additional competence to do a specific job or that a company has a strong basis from which to develop additional products. The term core competency was originally introduced in 1990 by the Harvard Business Review to describe the management concept of corporations possessing specialized expertise in a specific area. Competence is the functional /technical traits required to perform a job better. For example presentation skills for a marketing person, IT skills for systems guys. Competence is the ability to perform particular tasks and duties to the standard of performance expected in the workplace. In other words: doing the required task to the required standard. Competency is the behavioral traits required for the job to be performed better, e.g good communication skills, analytical skills, negotiation skills etc. Competency is the description of the knowledge, skills, experience and behavioral attributes necessary to carry out a defined function to the standard of performance expected in the workplace. In other words it is â&#x20AC;&#x153;the performance standardâ&#x20AC;?. The Competence is an ability to acquire through learning, exposures to the tasks and series of trainings. This ability is engaged to suffice the performance of a particular job. The skill wherein the little potential of an individual is given a chance to grow, mature, and immense so that adequate and appropriate steps will be exerted so that performance would be in accordance with the set of rules. Management of a company should look into the gaps of each member of the company in order to guide and give proper trainings to the concerned. In short, competence is acquired by chance or by choice. Meaning competence needs third party to invoke and push through the potentials of an individual. For example, the company is looking for an employee to go up the organizational hierarchy, and the competences needed were itemized. If an employee has no particular competence the company needs, he cannot fill up the post. In that case, because of the push by outside force, he is willing or force to find ways in order to acquire the needed competence. The competency framework is an important part of the performance evaluation system because this serves as a guideline on how to properly evaluate also serves a guideline that how well an employee is doing. Defining the key competencies will help the supervisor/manger to find an effective approach on how to handle an employee with poor productivity level and on how to further strengthen an employee with good performance. An

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established competency framework also serves as an effective tool in training needs analysis. To retain an employee is to promote professional growth & development within the organization. The framework will determine that what are the skills or competencies needed for an employee to develop its potentials and eventually improve his performance by creating more chances for growth. The competency framework serves as a backbone of an organization by giving support to the company and its employees. Without defining the key competencies, the employees wonâ&#x20AC;&#x2122;t have a clear direction and will have difficult to understand their very important roles in the organization. The framework is very helpful to both the company as well as its employees in achieving their goals for them to accomplish the mission and vision of the organization. One type of offensive corporate strategy by using the insights of transaction cost economics (TCE) in order to develop the concept of strategic disruption. According to TCE, a firm will establish a governance structure guided by the attributes of the transaction in question. This paper focuses on a critical case in world manufacturing and trade [1] the significant changes both in the strategic management field with the development of the competence-based management and use of the concept value. The design of the management field is with the diffusion of matrix based tools that helps to manage the interdependencies of the product, process and organization. [2] This paper documents research have conducted to investigate the existence and nature of core competency concepts within a section of UK Small and Medium sized enterprises (SMEs) manufacturing organizations [3] The causality relationship between automobile production quantities and automobile loan amount of deposit banks was analyzed with the assistance of co integration and VEC Models [4] The author suggested that the meaning of competitive advantage is self-evident, so there is no evident need to define its exact meaning. He supposed that differentiation based on key buying attributes of a product is the base of an advantage [5] the short-term competitive advantages are those which would last through a business cycle and long-term competitive advantages are those which would last over more than one business cycle [6]. The analytic hierarchy process (AHP) is an intuitively easy method for formulating and analyzing decisions. The analytic hierarchy process (AHP) provides a structure on decision-making processes having a limited numbers of choices but each has a number of attributes.[7] Factors such as government regulation of emissions, advances in technology, and increases in oil prices, the automobile market has entered into a period of flux and uncertainty. Vehicle manufacturers have reacted by developing several power train alternatives to the internal combustion engine (ICE). [8] II.

Case Study at Sonalika

Being Situated in the Punjab (The leading Agriculture state in India), Sonalika have deep knowledge of the farming requirement and also done an extensive research on the requirement and the actual need of the farmers, which resulted in development of the most suitable tractors and equipments for the Agriculture and commercial applications. International Tractors Ltd. has its own in house R & D setup which is recognized by Govt. of India. Ministry of Science and Technology Department of Scientific and Industrial Research. Ultra modern R&D facilities capable of Designing & Developing of components equipped with the following designing & Analyzing Facilities. Incorporated in 1995 International Tractors Limited has become a renowned name in tractor manufacturing industry. ITL, one of the top three tractor selling companies in India, has come of age to establish itself as a distinct and excellent tractor manufacturer. ITL is the proud manufacturer of the best selling tractors between 20 HP to 90 HP. The tractors manufactured by ITL bear testimony to greater performance, unmatched quality and higher reliability in the market because of their better pulling power, minimal fuel consumption and low emission. With their exceptional qualities these tractors cater to the demands of agriculture sector not just in India but also in various other countries. ITL tractors are fitted with engines which are compliant to smoke & mass emission for India certified by ARAI, Pune (Euro II) & also has approval from Environmental protection Agency, Washington DC for EPA compliance and from TUV for Euro III compliance. All tractor models of ITL are tested & approved by Central Farm Machinery and Tractors Training & Testing Institute, Bundi (MP) India, (the Government of India Institute authorized for issuing test reports). These Certifications have enabled ITL to enter in to various countries across the Globe. Moreover, the tractors manufactured by us are approved and tested dimensionally and structurally on the ground of specification, according to EEC and other international standards and homologations. So they offer our customers the best in class hydrostatic transmission, power steering, differential lock and advanced safety devices. ITL's top-end manufacturing processes, international quality control systems and advanced research and development facilities are duly ISO-9001:2008 certified by the joint accreditation system of Australia and New Zealand. Moreover, ITL is the first tractor manufacturing company in the country to be accredited with ISO14001 certification, TS16949. Today, taking the voyage of success further, Sonalika Group sits on a strong platform with a turnover of approx. 650 million USD. The annual average growth of 30% is the testimony of achievements and makes one of the fastest growing corporate in India. Moreover, Sonalika Group is also one of the few debt free corporate of the world. Its strength of 5000 people includes some of the renowned names in

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industry. Sonalika Group has also joined hands with some of the leading international names like Yanmar of Japan, JM Finance Trustee & Magma Sharachi Finance. Today Sonalika has become synonymous to prosperity, success and growth. With international technologies, state-of-the-art production units, experienced teams and tailor-made solutions Sonalika has responded really well to its customers' needs in India and abroad. The coming years will bring the same results for Sonalika, their associates and customers provided to go along with the same fervor and optimism. "To us at Sonalika Group, Quality is not something that we engineer, inspect and input into our tractors. It is an innate desire to attain the best that comes from within each of us. It defines our lives at work and at home and ripples out into the world around us". To maintain quality levels, every engine is also thoroughly tested on computerized machines after being manufactured.    

Producing world class Products in Quality and Reliability. Total customer satisfaction. Creating a culture among all Employees towards Total Quality Control Concept. Constant up gradation of Technology and Human Excellence

International Tractors Limited is an ISO 9001: 2008 for Tractor Assembly and TS16949 for In-house Gears and Shafts manufacturing. Sonalika Group firmly believes in matching the international Quality Standards to meet the Customer Satisfaction. To achieve this, Sonalika Group is always working on continuous focus on Quality for both its in-house manufacturing as well as chain of suppliers, and thrives to achieve excellence in Product Quality. The plant has in house latest technology to manufacture and ensure quality standards in HMS (Heavy Machine Shop), LMS (Light Machine Shop), Gear Shop and Press & Fabrication Shop. This includes chain of CNC Machines, VMCs, HMCs, PLC controlled Presses, Seal Quench furnaces, Gear Testers, 3D CMM and 2D CMM. Production staff and workers are technically qualified and regular trainings are imparted as per their job profiles besides focus on Zone wise 5S concept across the organization. ITL believes in Team Approach in the working with its chain of Suppliers through continuous up gradation in Process and initiatives of ' Direct On Line'. Our endeavor is to share voice of customer with suppliers at every stage. We encourage our suppliers to work for the concept of 'Cannot Make Defects' and 'Right First Time' at any stage of manufacturing. The main quality steps taken are: a) Quality Check of Gear Boxes b) A Total Grip on Quality Check The Dream Project of Sonalika group is to cater the agricultural and auto industry with quality abrasive products through untiring dedication and leadership. MISSION STATEMENT - We pay personal attention to our customers so that, we can build products they need, and not merely sell the products we build. Established in 1969, Sonalika group since the inception has tried to understand customer need to be facilitating them with its value for money products. To become the World's leading tractor manufacturing company and a major player in automotive products and services & to provide value for money to the customers by producing High Quality Innovative Products at competitive price the management are doing the following practices:    

Fast adaptability to change Offer service with a smile to customers Provide enjoyable working environments to employees Innovative in fields and business

The company was producing 140 units of tractors per day two years ago now its daily production capacity is 300 tractors and ramping up. The company is also increasing its dealers' network in India and will appoint over 200 dealers by the end of this year. ITL currently has 850 dealers in India. Sonalika is having collaboration with Yanmar, and raised its production to 200 tractors per day. Sonalika is the one of the top 3 tractor manufacturing companies in India, other products include of, Multi utility vehicles, engines and various farm equipments. Today the group stands tall with an approximate turnover of 5000 Crore INR. An average growth of 30% makes it one of the fastest growing corporate in India. It is also one of the few debt free companies. Group has strength of about 2500 employee technocrats. The trend reflects the changing dynamics in rural India where marginal farmers with small landholdings are going for tractors due to reasons including labor shortage and rising costs of bullock carts. Competition is on its heel, though. Punjab-based Sonalika Tractors is developing two categories of sub-Rs 2 lakh tractors including a specialized one for horticultural farmers who grow high-end specialized produce like orchids and maintain vineyards. A combination of factors labor scarcity and rising wages, better affordability of small marginal farmers and sales are plateauing in traditional markets like Punjab among others. Sonalika Tractors, meanwhile, expects its specialized tractor for horticultural farmers to be a hit because states like Haryana have been nudging their farmers to diversify into on higher-yielding horticulture crops. In the first quarter of the current financial year alone, 30 hectares of additional area were brought under fruit cultivation. Low horsepower tractors work

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well in soft soil conditions, such as river basins. At present, tractor penetration in Punjab, Haryana and parts of UP is almost saturated. The International Tractors Limited (ITL), a flagship company of Sonalika Group is a zero-debt company which is now looking at a much higher share of exports to grow. The ITL already exports to about 70 countries and has set up assembly lines in Nigeria, Cameroon, Algeria, Brazil, Germany and Argentina, co-owned with local distributors. Now, the idea is to focus on higher HP tractors and reach out to developed markets in Europe and the United States. There's a good reason for looking outside India — a tractor sold overseas fetches 30% higher margin than a unit sold locally. Table 1-Market analysis of the tractors in India (http://www.moneycontrol.com)

Progress during the past years 2010    2009  



Sonalika Tractors are now EURO –III A Norms Compliant Started Export of Tractors to Argentina & Serbia Executed the export order to Cameroon worth 40 Million US$. Grand Launch of technical advanced worldtrac series of tractors. The series comprises of tractors with advanced features. Developed in-house unique DIESEL SAVER UNIT FOR SONALIKA Tractors. Became only Company in India To Produce 90 Hp Tractors.

2008 



Launch of RX Series of Tractors. Aesthetically appealing designs and shape accepted across the world. Export of Tractors Started to USA.

2007



Joint Venture between International Tractors Ltd. and Magma Shrachi Finance.

2006

 2005   2001



Successfully Developed Four Wheel Drive front axles and Transmission of tractor for Yanmar. Achieved turnover of USD 235 Million. Joint venture with Yanmar of Japan for manufacturing of Tractors in India. Started in house manufacturing of engine for tractor application.

2000



Entered into Joint venture with Renault-France and Class-Germany,which helped the group to upgrade its technology and systems.

Currently, ITL has international sales of about 10,000 tractors a year, which accounts for 17% of its revenues. Going forward, Sonalika wants 30% of the increased sales of 100,000 tractors to come from exports. "We are at

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just 10% in India [market share]. By expanding reach and network, it shouldn't be difficult to get to 15-16%," in near future. In 2010, a Greenfield manufacturing facility was commissioned at Fatuah, Bihar, with a capacity of 20,000 units a year, while last year the Hoshiarpur factory was expanded by 20,000. III.

Conclusion

The following conclusions have been made.   

The both tractor manufacturing units are technically sound and is fully equipped with the latest & precise machinery. Due to the spam of time the profitability of both the tractor manufacturing unit has been improved. Due to the joint venture of Sonalika the market share has enhanced more than the other during the past time. IV.

[1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16]. [17].

References

Averyt & Ramagopal (1999): Strategic disruption and transaction cost economics: The case of the American auto industry and Japanese competition. Bettis, R. et al., (1996). Dynamic core competencies through metal earning and strategic context. Journal of Management. Bhamra et al., (2010): Competence understanding and use in SMEs:a UK manufacturing perspective. Celik et al., (2012): The relationship between automobile loans and automobile production amount as a key factor for production strategy. Clair et al., (1996): Transport network design and mode choice modeling for automobile distribution: a case study. Emad Y. Masoud (2013): The Impact of Functional Competencies on Firm Performance of Pharmaceutical Industry in Jordan. Wee et al., (2012): Technological diversity of emerging eco-innovations: a case study of the automobile industry. Andrew Williams (2006): Product service systems in the automobile industry: contribution to system innovation. Dae-Ho Byun (2001): The AHP approach for selecting an automobile purchase model. Davoine (2008): Functional competencies and their effects on performance of manufacturing companies in Vietnam. Haartman(2012): Manufacturing capabilities: Mere drivers of operational performance or critical for customer-driven innovation. Jayaram et al., (2007): The Effects of Human Resource Management, Manufacturing and Marketing strategies on Competitive strategy and Firm Performance in an Emerging Economy Jensen et al., (2010): Manufacturing 2025 Five future scenarios for Danish manufacturing companies. Moore (2011): Identity, knowledge and strategy in the UK subsidiary of an Anglo-German automobile manufacturer. Moreno Muffatto (1998): Reorganizing for product development: Evidence from Japanese automobile firms. Narelle Kennedy (2011).Manufacturing Futures A paper by the Australian Business Foundation for the NSW Business Chamber. Reijnders et al., (2012): The effect of corporate social responsibility on consumer satisfaction and perceived value: the case of the automobile industry sector in Portugal.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net BIM creation using point clouds Desislava Georgieva Tanusheva Faculty of Geodesy University of Architecture, Civil Engineering and Geodesy (UACEG) Sofia 1046, 1 Hristo Smirnenski Blvd. Bulgaria Abstract: The report examines the technology of laser scanning objects using laser scanners, the accuracy of the collected spatial data and its application in the process of creating a Building Information Model (BIM). The aim of the study is to present the technology to efficiently create 3D object-oriented digital models from scanned data. The results obtained can be used in measuring, modeling and certification in construction sites, monuments, civil engineering structures - bridges, tunnels, linear objects, cuttings, embankments and other. Keywords: laser scanning, point clouds, object-oriented modeling, BIM, visualization I. Introduction BIM is abbreviation of Building Information Modeling. Using BIM we create and manage digital records for a project throughout its life - from the earliest concepts and designs of the architects, in the design solutions of the engineers, through the work of the builders and then the maintenance and management of the building during its service life and eventual destruction. The concept of BIM has existed since the 1970s.. For the first time the term was used in 1992 in an article by G.A. van Nederveen and F. P. Tolman [2]. However, the term Building Information Modeling (including the acronym "BIM") became popular when Autodesk released an article entitled "Building Information Modeling" in 2003. II. BIM nature A. The concept of BIM In an article for the CIO magazine, 2013. [3] Dr. Arch. Boyan Georgiev explained that BIM is an acronym that combines three different but interrelated concepts (Fig. 1):  Building Information Modeling is the process of creating and exchanging data on the building (structure) during its design, construction, use, maintenance and destruction, that in its lifetime. BIM allows participants in these processes access the same information through the interaction of different technological platforms. Figure 1. The concept of BIM



Building Information Model is a digital description of the physical and functional characteristics of the building (structure) that serves as a shared source of data and information. It forms and maintain a reliable database for decision-making during its existence.

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

Building Information Management provides organization and control of business processes using information from the digital model to permit the exchange of information throughout the lifecycle of the facility. The benefit of this is the presence of a centralized and visualize information, pre-study opportunities, sustainable solutions, effectively design, integration of disciplines and systems, complete construction and technical documentation and more.

B. BIM benefits Here BIM is seen as an object-oriented digital model (OOCAD) with full database. To create models in a graphical environment no longer work but with lines and types of objects to which is associated attribute information [4]. Each created BIM model of a building or facility can be regarded as a GIS application for managing the design, construction and its service period. Its capabilities include:  The engineers used BIM for complex analyzes of the efficiency of the building structure and systems of the building, and ultimately create construction documentation and significantly reduce the time for calculating and drawing. Creative work focuses on the design;  Builders use BIM for more efficient management of resources needed for the project (type and quantities of materials, personnel and equipment).  Investors make quick financial analysis for their investment, saving costs by the lack of conflicts, accelerated development and effective maintenance of the building.  Introduction of 5D BIM - a concept widely used in the CAD industry, which refers to the relationship between 3D graphic components of the model and defined time schedules and information related to the cost of various activities and materials. C. BIM development In recent years [3] in the UK BIM concept is the basis of public policy. At the beginning of 2008 was published standard BS 1192, which is applicable to all parties involved in the preparation and use of information during the design, construction, operation and demolition. Leadership of the United Kingdom reported positive by the European Commission. In late October 2013 a conference was held to discuss the EU Directive on the application of BIM in the EU. The first step in this direction has already been made. From the end of March 2013. applies ISO 16739:2013 - Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries. In the Netherlands in 2012 was published a second version of the standard defining the parameters of the documentation and formats for the implementation of BIM in construction. In the United States without a special institution or organization dealing with the use of BIM, the big design companies are actively pursuing this type of technology. Among them are the leading software companies that use the concept of BIM and actively assist private companies and government institutions for implementing it Asian countries also noted the development of their attitude to BIM. In Singapore adopted regulations for the mandatory introduction of these technologies. South Korea follows the same route. India is developing high potential of export firms manufacturing these technologies to help industrialized countries. III. BIM & 3D LASER SCANNING The application of the technology of laser scanning (Fig. 2, 3 and 4) is popular in surveying and GIS environments for many years. However, recent advances in hardware technology and development of BIM help usher in a new level of data use of laser scanning in the construction industry. Scanning for the purpose of construction is most often applied to existing sites (eBIM or existing BIM), but appear and applications related to new construction and design. Scanning technology is becoming an essential step to complete the integrated BIM cycle and plays an important role in the process. Figure 2. From scanned "point cloud" to BIM documentation for future applications

Many of the companies producing laser scanning instruments (laser scanners) have issued guidance on how the scanning technology can be applied in the BIM workflow and construction [1]. Scan technology can be used for

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optimizing the building process at the same time reduce the risk of the project, the cost and the time of its completion, respectively realization. The objective is to understand the practical application of the laser scanning, as regards the object-oriented modeling and integration in BIM. Figure 3. Exterior scanning

Figure 4. Interior scanning

The essence of the process of laser scanning is as follows. Scanners send laser beams with high density scanning the object and perform measurements to determine the position of a lot of points. The hardware of the scanner measures the time of elapsing the distance from the scanner to the reflecting surface and back to its adoption by the scanner ( in pulsed scanners ) or the phase difference ( in phase scanners ). Thus the distance and spatial angles between the scanner and scanned object for each his point is measured. Contemporary scanning technology has the ability to send thousands of beams per second, resulting in a huge amount of collected spatial data, known as „point cloud „. Many scanners can identify and record the RGB (Red, Green, Blue) value or intensity of each reflected point from the object. As a result, point clouds include millions or even billions of data, which describes the scanned area maximum realistic. Figure 5. 3D model of a metro Figure 6. 3D model of a bridge construction

3D object-oriented design process presented on 3D terrain can be called 3D vertical planning. Combining the design of the building or facility with real property serves to create realistic models of the designed object, illustrating the investment intentions Figure 7. Designing using the scanned area

. IV. Laser scanning data processing in Revit A. Introduction of point cloud and its application to a local coordinate system Scanned data converting into BIM model is a classic process of data transformation. First is performing the scanning process scans from required number of stations. Second, the collected data from different stations is combined into one, which is commonly known as post-processing, or the step of registration. Figure 8 shows the unified and processed point cloud of a church [7] in 3D view visualization mode. The scan process is accomplished with Leica ScanStation C10. Figure 8. Unified and processed point cloud of Holy Trinity church, Sofia, Bulgaria

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Scanning can be time-consuming initiative, leading to a very large and / or complex data sets, so it is recommended each team that applies this technology to plan very carefully their efforts ( Fig. 9). The desired outcome of the scanning process should be clearly defined. In many cases, the desired result is to accurately determine the location (X, Y, Z coordinates) for the physical surface of the object. In other cases, the required information can be information 4D (3D + time schedule), and cost information, 5D. Last but not least, creating models can be supplemented with the management of the facility. Figure 9. Scheme of the location of scanning stations

B. Defining levels With Level tool we can identify the main sections. A level of any desired height is created by the designer of the building (e.g., the first floor, the upper part of the concrete wall, etc.). Adding levels takes place in elevation view visualization mode (Fig. 10). Figure 10. Defining levels

C. Plan view By defining the existing levels of the building or facility shall be established and their respective plan views (fig. 11). The main part of the design work is accomplished in plan view visualization mode. Figure 11. Plan views (2D)

D. Constructive components (walls, floors, roofs, rooms) The software supports a high level of topology and performs continuous monitoring of the underlying structures (fig. 12). An interesting issue is the accuracy of the attachment of a defined structural element to a specific set of points from the cloud. The research about the accuracy of the selection of smoothing straight lines and planes is subject of a future work. Figure 12. Basic constructions /roof/

Figure 13. Basic constructions /wall/

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E. Adding families and edit them The different kind of objects like doors, windows, columns, etc. are loaded in the project from external libraries â&#x20AC;&#x201C; files with extension .rfa Figure 14. Adding families

F. Selection of suitable materials for the constructive components If in the attribute information of the constructive components is introduces information about the material they are made of, the project is well illustrated in realistic view visualization mode. Figure 15. Realistic view

G. Sections By defining different sections for the purposes of the geodetic practice we easily form an orthophoto image. In the design process sections and defined network design axes (grids) are part of the documentation required for the project. The axes may be defined as lines, arcs, or a combination thereof. Figure 16. Sections

H. Statements of graphic and attribute database One of the main advantages of this modern way of designing is the possibility of implementing fast and easy extracts attribute information attached to each object in the model (Fig. 17). For example, all the bill of quantities are displayed automatically. This saves time, effort and reduces the possibility of error admit in the calculations. Figure 17. Table value of all walls of a given type

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I. Plot design and export to other formats The software offers data export to other CAD applications (Fig. 18). Figure 18. Vector model

V. Conclusions The main features and benefits of BIM are discussed in the report. The disadvantages accompanying introduction of new software in the design and maintenance of buildings [4] should be noted too. First problems in documenting the final products of built BIM for existing country standards and practices accordingly. Introduction of national standards and libraries for graphics and documenting the final material supporting all design activities for the project. It is important that as BIM software system requires hardware platform with greater possibilities of commonly used at present. There is also a need for time and resources for training and utilization of BIM specialists in design and construction. In conclusion, the modeling of the considered object and its transformation from a scanned point cloud in semantically enriched object-oriented 3D digital model in Revit software can be summarized that this modern approach of designing gives results in a high precision, detail and reality. It saves time and money, creates a product ready for use and subsequent upgrade by all other professionals working on its reconstruction, restoration, or external repairs and more. References 1. 2. 3. 4. 5. 6. 7.

Gleason D. , Laser Scanning for an Integrated BIM, Constance, October, 2013 http://en.wikipedia.org http://cio.bg http://blog.tsukev.com http://www.cadpoints.com http://wikihelp.autodesk.com http://www.geocad93.com

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Employer Branding: The New Mantra for Talent Acquisition 1

Mrs. Shipra Sharma, 2Ms. Sakshi Chhabra Centre for Development of Advanced Computing B/30 Academic Block, Sector 62, Noida 2 Plot No.4 GYS Heights, MasTech Sector 125, Noida ________________________________________________________________________________________ Abstract: This paper encompasses the new area of research exploring the thought where brand is not just a symbolic embodiment of all the informations connected to a company, product or service but also embrace its employees.” This research thus covers the idea of creating employer’s brand which is the image seen through the eyes of current and potential employees. Making the place attractive for new talents and also keep the potential employees motivated for being the part of the organization. The report aims at understanding the fact that building a strong employer brand image in market by an organization act as a major source for talent acquisition. The following 6 Employer Brand dimensions are considered for current study they are Product & Services, Business performance, Pay & Benefits, Management, employer behavior and work life balance. The data will be collected through questionnaire (measured on 5-point likert scale).This study quantifies the effects of employer branding on talent acquisition on the basis of above dimensions on job seekers. The research is followed by findings and recommendations. Keyword: Employer Branding (EB), Talent Acquisition ____________________________________________________________________________________ 1

I. Introduction The concept of employer branding was developed at the end of 1990s in consequence of tightening labor markets. Ever since, the idea has developed on the minds of both human resource and marketing professionals. However, the concept remains controversial and it is still responded skeptically. It has become harder to attract talents and companies have to make long term employer branding strategies. (Taylor, 2010) Ballantyne introduced the “six markets” model which highlights the six stakeholder markets that a company should have a relationship with, including the recruitment and internal markets (2002). For customers a company communicates with its company or product brand, whereas for these two markets a company should communicate with its employer brand. This does not mean that the company would have several separate brands, but that it has several aspects in its one brand. It has to communicate different messages via different channels depending on whom the message is targeted at. Potential employees are interested in different things than consumers. At present time this term is very much widespread almost all over the world and it keeps on spreading and most of the companies are applying the concepts and principle about the subject. As a matter of fact most of the companies consider employer branding as one of the most integral part in their business strategy. This is testified in different countries such as in USA, India, Australia, Asia, and Europe. In the changing business scenario corporate brands have become enormously valuable assets. The Employer Branding is evolving, to increase the attention towards attracting and retaining potential employees in any nature of the organization. Companies with a strong and positive corporate brand image have two advantages; they are an ability to have a single umbrella image that throws light on a range of products (corporate branding giants like Sony, Disney and Microsoft) second an ability to attract and retain the best talents in the market and get a commitment to quality from them (Yahoo, Southwest Airlines and Infosys). II. Methodology A. Choice of Method The aim is to find relationships between variables by studying different situations or problems as well as try to find new insights into new phenomena, leading to a mixture of an exploratory and an explanatory research design. Null hypothesis considered is an Employer Branding (EB) element play a major role in talent acquisition is supported and clarified by theories1. The hypothesis being followed by EB elements for talent acquisition of job seekers have been tested using the data collected from and evaluated using Chi Square method. III. The relation between Employer branding and Talent Acquisition In today’s brand conscious era where everyone is driven by branded products, services it has become more of a status symbol and this has not kept our organizations untouched. Even they are striving for creating a brand image which has forced them to invest huge capital on branding the company image. This has lead them to meet their objective –one hitting the competitive market with high profit margins and the other which has captured the attentions of the recruiters, is creating a market for new and potential talents. This has led the companies to enter in a “War of Talent” that encounters the fight between the employees to recruit and retain the correct hire.

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Looking at the supply statistics from the view point of talent, one can conclude that the population is ageing. The trend is projected for Germany, where by 2025 the number of people between 15 to 64 age group will fall by 7 percent and by 9 percent in Italy. The trend happens to be more drastic for China because of one-child policy. At the same time candidates equal and above 55 age group desires to work longer in order to afford later retirement. This scenario will soon lead to leadership drain. Hence, it becomes necessary for the organization to create a brand for its employees keeping them loyal to the company and for the potential candidates to visualize organization as a dream company to get associated with. These two aspects would direct the employer to attract and retain potential talents resulting in increased productivity creating an image of the company through its employees. Creating Employer brand

Employer Attraction & Retention

Employee Loyalty

Talent Management

Talent Association Figure 1: Relationship between Employer Branding and Talent Management

A. Key Elements of Employer branding S.No.

EB Dimensions

Description

Key Questions

Products & Services

Do people trust and praise your products and services? Are they competitive? Does your organization well think of in the marketplace?

Business Performance

Products are tangible and discernible items that the organization produces and Services refers to the production of an essentially intangible benefit, either in its own right or as a significant element of a tangible product, which through some form of exchange, satisfies an identified need. This refers to the business’ independent criteria to assess its overall outcomes, in relation to its own goals.

Pay and Benefit

This refers to the payment schemes and other benefits given to the employees

Management

Employer Behavior

Work Life Balance

This refers to the dealing with or controlling things or people: "the management of deer" This refers to an attitude of an employer towards candidates in favorable and non favorable situations It refers to the balance between employee’s personnel life and work life

Is your organization stable, profitable and growing? Are they scandal free? Do suppliers respect your organization? Does the organization provide handsome salary and other benefits? Does your organization offer an admirable leadership? How employer is treating the candidates? Does your organization keeps a work life balance and provide a good quality of work life?

IV. Analysis A. Research Problem The primary research question is: “What kind of decision making process occurs when a job seeker applies for a job?” The research aims to answer these questions: (i)What information interests job seekers? (ii)What job qualities are appreciated by job seekers? (iii)Do the theoretical benefits of employer branding exist? The objective of the primary research is to find out which qualities of employers are appreciated by job seekers and whether the above mentioned parameters affect the decision of job seekers while applying for jobs. B. Research Design and Sampling Technique The type of research design used in the project was Descriptive research, because it helps to describe a particular situation prevailing within an organization. Simple random sampling method was used in this project. Since population was not of a homogenous group, Stratified technique was applied so as to obtain a representative sample. The employees were stratified into a number of sub-population or strata and sample items (population with different qualification) were selected from each stratum on the basis of simple random sampling. C. Tools Used For Analysis  Percentage Analysis  Chi-Square.  Five point likert scale.

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D. Data Seggregation All the data has been collected, usable responses are put on for consideration and summarised in Table 1 shown below. The proper analysis on the basis of 6 EB parameters for further research Process is in progress which will include the basic issues related with Employer branding and the ways to overcome them. Table I-A Group Students(A) Graduates (B) Post Graduates (C) Experienced(D)

Number of Respondants 5 13 10 7 35

Total

Percentage 14.28 37.14 28.57 20 100

120 100 80 60 40 20 0

Number of Respondants

ot al T

os t

G ra

S tu de

nt s( A

) du at es G (B ra ) d ua te E s xp (C e ) rie nc ed (D )

Percentage

E. Inference It is seen from the table 1-A that 37.14 % of the sample population is Graduates, 28.57 % are Post graduates, 20 % are Experienced and 14.28 % are Students. F. CHI-SQUARE Test For checking the hypothesis for EB Parameters:Here, Group A- Students Degree of satisfaction is defined by Likert scale i.e Group B- Graduates 5-Strongly agree Group C- Post Graduates 4-Agree Group D- Experienced 3-Neutral 2-disagree 1-Strongly Disagree Significance level is taken as 5% Degree of Freedom=(r-1)(c-1) = (4-1)(5-1)=12 Tabulated value of Chi-Square at significance level of 5 % and degree of freedom as 12 =21.026 A) Products & Services H0:-All the groups of job seekers are attracted by Products and services provided by organization. H1:-All the groups of job seekers are not attracted by products and services proided by organization. Table-III-A Group A B C D Total Observed Frequency (O) 4 3 3 5 1 6 7 1 0 4 0 1 0 Calculated Value

5 4 3 3 5 15

4 1 6 7 1 15 Expected Frequency (E) 2.142 4.285 4.285 3 2.142 5.571 4285 3 0.714 1.857 1.428 1 0

3 0 4 0 1 5

2 0 0 0 0 0

1 0 0 0 0 0

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

1.858 -1.285 -1.285 2 1.142 0.429 2715 -2 -0.714 2.143 -1.428 0 0

3.452 1.651 1.651 4 1.304 0.184 7.371 4 0.509 4.52 2.039 0 0

1.611 0.384 0.384 1.33 0.608 0.033 1.720 1.33 0.712 2.472 1.427 0 0 12.814

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Therefore, Calculated value of chi-square = 12.814 B) Business Performance H0:-All the groups of job seekers are attracted by business performance of organization . H1:-All the groups of job seekers are not attracted by business performance of organization. Table-III-B Group A B C D Total

5 2 1 1 1 5

Observed Frequency (O) 2 1 1 1 1 5 4 10 2 1 5 2 0 Calculated Value

4 1 10 4 5 20 Expected Frequency (E) 0.714 1 1.428 1.857 2.857 4 5.714 7.428 1.428 2 2.857 3.714 0

3 2 2 5 1 10

2 0 0 0 0 0

1 0 0 0 0 0

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

1.286 0 -0.428 -0.857 -1.857 1 1.714 2.527 0.572 -1 2.143 1.714 0

1.653 0 0.183 0.734 3.448 1 2.937 6.615 0.327 1 4.592 2.937 0

2.315 0 0.128 0.395 1.206 0.25 0.514 0.890 0.228 0.5 1.607 0.790 0 8.823

Therefore, Calculated value of chi-square = 8.823 C) Pay & Benefits H0:-All the groups of job seekers are attracted by Pay & benefit schemes provided by an organization. H1:-All the groups of job seekers are not attracted by Pay & benefit schemes provided by an organization. Table-III C Group A B C D Total Observed Frequency (O) 1 1 5 3 2 3 4 7 1 2 0 3 1 1

5 1 3 5 1 10

4 2 7 4 3 16 Expected Frequency (E) 1.428 2 2.857 3.714 1.857 3.2 4.571 5.942 0.857 1.2 1.714 2.22 0.428 0.6

3 1 3 0 2 6

2 1 0 1 1 3

1 0 0 0 0 0

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

0.428 -1 2.142 -0.714 0.142 -0.2 -0.571 1.057 0.142 0.8 -1.714 0.77 0.571 0.4

0.183 1 4.591 0.510 0.020 0.04 0.326 1.111 0.020 0.64 2.938 0.595 0.326 0.16

0.128 0.5 1.6072 0.137 0.0023 0.0125 0.071 0.1880 0.0023 0.533 1.714 0.268 0.76 0.26

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1 0 0

0.857 1.14 0

0.142 -1.14 0

0.020 1.241 0

0.023 1.14 0 7.354

Calculated Value

Therefore, Calculated value of chi-square = 7.354. D) Management H0:-All the groups of job seekers are attracted by Management at organization. H1:-All the groups of job seekers are not attracted by Management at organization. Table-III D Group A B C D Total

5 3 1 1 0 5

Observed Frequency (O) 3 0 1 1 0 5 5 10 1 1 2 1 1 1 2 1 0 Calculated Value

4 0 10 5 5 20 Expected Frequency (E) 0.714 1 1.428 1.857 2.857 4 5.714 7.428 0.714 1 1.428 1.857 0.714 1 1.428 1.857 0

3 1 1 2 1 5

2 1 1 2 1 5

1 0 0 0 0 0

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

2.286 -1 -0.428 -0.857 -2.857 1 -0.714 2.58 -0.286 0 0.572 -0.857 -0.714 0 0.572 -0.857 0

5.225 1 0.183 0.734 8.162 1 0.509 6.656 0.081 0 0.327 0.734 0.509 0 0.327 0.734 0

7.31 1 0.128 0.39 2.85 0.25 0.089 0.089 0.114 0 0.229 0.39 0.712 0 0.229 0.39 0 14.879

Therefore, Calculated value of chi-square = 14.879. E) Employer Behavior H0:-All the groups of job seekers are attracted by Employer behavior in organization. H1:-All the groups of job seekers are not attracted by employer behavior in organization. Table-III E Group A B C D Total Observed Frequency (O) 2 1 5 11

5 2 11 5 1 19

4 2 1 2 2 7 Expected Frequency (E) 2.714 3.8 5.428 7.057

3 1 1 2 2 6

2 0 0 0 1 1

1 0 0 1 0 2

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

-0.714 -2.8 -0.428 3.94

0.509 7.84 0.183 15.546

0.187 2.063 0.022 2.202

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2 2 2 1 1 2 2 1 0 1 0 0 0 1 1 0

1 1.4 2 2.6 0.857 1.2 1.714 2.22 0.142 0.2 0.285 0.371 0.285 0.4 0.571 0.742

1 0.6 0 -1.6 0.142 0.8 0.28 -1.22 -0.142 0.8 -0.285 -0.371 -0.285 0.6 0.428 -0.742

1 0.36 0 2.56 0.020 0.64 0.081 1.488 0.020 0.64 0.081 0.137 0.089 0.36 0.176 0.551

1 0.257 0 0.984 0.023 0.533 0.0472 0.670 0.143 3.2 0.285 0.371 0.285 0.9 0.308 0.742 14.233

Calculated Value

Therefore, Calculated value of chi-square = 14.232. F) Work Life Balance H0:-All the groups of job seekers are attracted by work life balance in organization. H1:-All the groups of job seekers are not attracted by work life balance in organization. Table-III F Group A B C D Total

5 1 1 3 2 7

Observed Frequency (O) 1 2 3 1 2 2 5 10 1 2 1 1 1 1 0 0 0 1 1 1 Calculated Value

4 2 10 5 2 19 Expected Frequency (E) 1 1.4 2 2.6 2.714 3.8 5.428 7.057 0.714 1 1.428 1.857 0.142 0.2 0.285 0.371 0.428 0.6 0.857 1.114

3 1 1 1 2 5

2 1 0 0 0 1

1 0 1 1 1 3

Total 5 13 10 7 35

O-E

(O-E)2

(O-E)2 / E

0 0.6 1 -1.6 -0.714 -1.8 -0.428 2.942 0.285 1 -0.428 -0.857 0.857 -0.2 -0.285 -0.371 -0.428 0.4 0.142 -0.114

0 0.36 1 2.56 0.510 3.24 0.183 8.660 0.081 1 0.183 0.734 0.734 0.04 0.081 0.137 0.183 0.16 0.020 0.013

0 0.257 0.5 0.984 0.187 0.852 0.033 1.227 0.114 1 0.128 0.395 5.169 0.2 0.285 0.371 0.428 0.26 0.023 0.0117 12.424

Therefore, Calculated value of chi-square = 12.424.

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INFERENCE:It is seen from the above calculations that tabulated value of Chi-square is more than the calculated value for each Employer Brand parameters i.e. Product & Services ,Organizational Culture , Business performance ,Pay & Benefits , Management, Social Values , employer behavior and work life balance which states the fact that null hypothesis taken is correct, i.e. Job seekers of all groups are attracted by Employer Brand parameters while making their decision to work in a particular organization. V. Findings & Recommendations A. Key Findings 1. It is seen from the table that 37.14 % of the sample population is Graduates, 28.57 % are Post graduates , 20 % are Experienced and 14.28 % are Students. 2. It is seen that from the above calculations that tabulated value of Chi-square is more than the calculated value for each Employer Brand parameters i.e. Product & Services ,Organizational Culture , Business performance ,Pay & Benefits , Management, Social Values , employer behavior and work life balance which states the fact that null hypothesis taken is correct. This represents that Job seekers of all groups are attracted by Employer Brand parameters while making their decision to work in a particular organization. B. Recommendations Employer branding not only act as a source of talent acquisition but actually plays a major role in Talent Management in organization. Employer branding can be a worthwhile marketing investment, when done right. As in any other marketing activity, the company needs to know the target group in order to attract them. In case the company knows what students and graduates want and what are their needs, they can communicate the things they have to offer, that meet the needs and wants. The employer branding process is continuous and it should be as carefully planned. The society is becoming more and more service centered and people are the asset than can lead a business to success. Maslow has created a hierarchy of needs where all human needs are placed in ascending order of its importance. 1) Physiological needs are basic needs sustaining human life. 2) Safety needs are need of being free of physical danger and of the fear of losing job, home etc. 3) Affiliation needs are needs to be accepted. 4) Esteem needs occur when a person has started to satisfy its need for belonging. After this, people need to be held in esteem by themselves and others. These needs produce satisfactions such as power and prestige. 5) Need for self-actualization is the highest need. This is the desire for one to become what itâ&#x20AC;&#x2122;s capable of becoming. Employer branding not only act as a source of talent acquisition but actually plays a major role in Talent Management in organization. Employer branding can be a worthwhile marketing investment, when done right. As in any other marketing activity, the company needs to know the target group in order to attract them. In case the company knows what students and graduates want, they can communicate the things they have to offer, that meet the wants. The employer branding process is continuous and it should be as carefully planned. The society is becoming more and more service centered and people are the asset than can lead a business to success. VI. Conclusion This research has proven that employer branding can have a positive effect from the company point of view on the job application processes of job seekers. However, the company needs to communicate the benefits it has to offer that meet the needs, wants and desires of job seekers. As in all marketing communications, the company has to communicate the right message via the right channel to succeed. The main media of communicating employer brand happens through Internet, which is mostly accessed by students and graduates. Internet presence is highly important because the information search happens mostly on portals, websites and soon. This presence is needed at the places where applying process takes place, e.g. job portals and search engines. However, companies should also have an employer presence at their own website. They should invest in having a variety of updated information at the Internet, because the information search can be assumed to be wider than at the consumer decision making process. It is harder to withdraw after accepting a job compared to buying behavior and the person needs to be fairly sure that the decision is right. In order the company to make sure potential employees choose them, they need to, besides spreading information, they also need to highlight their benefits at the announcements and attract them at the interviews. This means that the companies should invest in creating distinctive announcements which are the advertisements for the available positions. Bibliography [1]. [2].

Baack, D. and Clow, E. K. (2002) Integrated Advertising, Promotion & Marketing Communications. New Jersey: Pearson Education Inc. Ballantyne, D., Christopher, M. and Payne, A. (2002) Relationship Marketing: Creating Shareholder Value. Rev. Edition.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Analysis of Risk Management of Vendor in Banking Dr Hariharan.N.P1, Reeshma.K.J 2 Professor of Economics, 2Research scholar 1,2 School of Social Sciences and Languages, VIT University, Vellore-14, Tamil Nadu, INDIA 1

Abstract: Core banking across the globe has resulted in outsourcing of a number of activities of the banks. Vendors have come to stay in the banking industry. Though the vendors discharge a number of functions of the banks, they do not have accountability and do not own failures. Vendors provide banks with as much of comforts and also inconveniences. Banks have to develop measures to safeguard their interests and that of their customers. There have been guidelines globally as to how to deal with the vendors, and in India, there are specific guidelines given by RBI. The paper deals at length the risks associated with banks while dealing with vendors and suggests ways and means to mitigate the risks. Keywords: Core banking, risks, outsourcing, investment banking. I. Introduction Before the introduction of economic reforms in 1990s, banking was a simple business like most other business of those years. With core banking, it has become impossible for the banks to manage the affairs with their own staff, because soft ware technology (an integral part of core banking) is not a strong point for an appreciable percentage of bank employees. After the introduction of core banking, vendors have started playing major role in banking activities across the globe. With increasing dependence on vendors, the banks are taking risks. While third-party vendors may be developing the products or providing outsourced operations, the banks that hire them may be stamping these products and services with the bank’s name. If a data breach occurs, a disruption to online banking services strikes or a product or service misfires, customers are likely to point to the bank, not the vendor. II. Reasons for the Presence of Vendor Financial institutions frequently use third-party vendors to reduce costs, enhance performance, and obtain access to specific expertise. Examples include outsourcing audits, compliance reviews, disclosure preparation, data processing, and website development. Financial institutions also use third-party vendors to offer products directly to customers. It is important to emphasize, however, that while day-to-day management of a product or service can be transferred to a third party, ultimate responsibility for all compliance requirements cannot be delegated and remains with the financial institution. Thus, institutions should recognize that using vendors involves significant compliance risk. At many institutions, vendor-management programs have focused predominantly on risks to the bank and the financial system-specifically, on business continuity, financial strength, and credit risk. With the scope of regulatory oversight broadening to include the consumer, many firms are underprepared. But since financial institutions must bear the responsibility for their suppliers’ misdeeds, they must improve the way they manage these relationships. In response to the changes, financial firms are looking for new solutions to identify and manage third-party risk. A number of leading banks and credit-card companies are developing and embracing best practices. III. Global Guidelines Operational Risks: Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal risk, but excludes strategic and reputational risk. Operational risk is inherent in all banking products, activities, processes and systems, and the effective management of operational risk has always been a fundamental element of a bank’s risk management programme. As a result, sound operational risk management is a reflection of the effectiveness of the board and senior management in administering its portfolio of products, activities, processes, and systems. The first step in risk management is to identification, and measuring exposures to risk. There should be effective capital planning and monitoring programme. Risk exposures should be monitored and steps should be taken to

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control or mitigate risk exposures. There should be periodical reporting to senior management and the board on the bank’s risk exposures and capital positions. Verification of the Framework is done on a periodic basis and is typically conducted by the bank's internal and/or external audit, but may involve other suitably qualified independent parties from external sources. Verification activities test the effectiveness of the overall Framework, consistent with policies approved by the board of directors, and also test validation processes to ensure they are independent and implemented in a manner consistent with established bank policies. Validation ensures that the quantification systems used by the bank is sufficiently robust and provides assurance of the integrity of inputs, assumptions, processes and outputs. Specifically, the independent validation process should provide enhanced assurance that the risk measurement methodology results in an operational risk capital charge that credibly reflects the operational risk profile of the bank. In addition to the quantitative aspects of internal validation, the validation of data inputs, methodology and outputs of operational risk models is important to the overall process. IV. Guide lines by RBI As a step towards enhancing and fine-tuning the risk management practices as also to serve as a benchmark to banks, the Reserve Bank had issued Guidance Notes on management of credit risk and market risk in October 2002 credit and market risk. ‘Management’ of operational risk is taken to mean the ‘identification, assessment, a nd/or measurement, monitoring and control/mitigation’ of this risk. Highly Automated Technology: If not properly controlled, the greater use of more highly automated technology has the potential to transform risks from manual processing errors to system failure risks, as greater reliance is placed on integrated systems. SRAS automates and orchestrates enterprise IT security and risk management. SRAS simplifies and integrates network discovery, baseline configuration management and vulnerability management enabling reporting for enterprise risks and regulatory compliance. It offers flexible agent-based or agent-less data gathering options across multiple hardware and software platforms. SCAP validated, enterprise proven. Emergence of E- Commerce: Growth of e-commerce brings with it potential risks (e.g. internal and external fraud and system securities issues) Some of the risks associated with e commerce are Copyright infringement and invasion of privacy suits stemming from digital scanning and morphing, Flight of intellectual property due to employees moving to competitors, electronic bulletin boards containing defamatory statements resulting in liability or embarrassment. Internal Controls: Emergence of banks acting as very large volume service providers creates the need for continual maintenance of high-grade internal controls and back-up systems. The Basel Committee is distributing this paper to supervisory authorities worldwide in the belief that the principles presented will provide a useful framework for the effective supervision of internal control systems. More generally, the Committee wishes to emphasise that sound internal controls are essential to the prudent operation of banks and to promoting stability in the financial system as a whole. While the Committee recognises that not all institutions may have implemented all aspects of this framework, banks are working towards adoption. The guidance previously issued by the Basel Committee typically included discussions of internal controls affecting specific areas of bank activities, such as interest rate risk, and trading and derivatives activities. In contrast, this guidance presents a framework that the Basel Committee encourages supervisors to use in evaluating the internal controls over all on- and off-balance sheet activities of banks and consolidated banking organisations. The guidance does not focus on specific areas or activities within a banking organisation. The exact application depends on the nature, complexity and risks of the bank's activities. Outsourcing: Growing use of outsourcing arrangements and the participation in clearing and settlement systems can also present significant other risks to banks. The vendor landscape has over 50 large and small vendors. Large BPOs like WNS and Genpact traditionally catering to the international market are focusing on building their domestic BPO divisions. We have classified the vendor landscape in the domestic banking BPO into four categories: a) International leaders (established BPO vendors with strong presence in international BPO market like MphasiS) b) India leaders (primarily focused on domestic market like Aegis, InfoVision and Omnia BPO) c) Emerging companies (companies building capabilities and currently offering specialized services on a small scale like Caretel, vCustomer) d) 'Me-too’ players (offering undifferentiated low value services) Large scale multi-city operations, partnerships, ability to quickly ramp up operations while maintaining quality and developing advisory capabilities will be key success factors for outsourcing vendors focused on the banking industry. As per reports, with further de-regulation in the banking industry and entry of several new private and foreign banks, the addressable market for banking BPO is expected to grow to 10 times the current revenues.

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V. Role of Investment banks Large Scale Acquisitions, Mergers, de-mergers and consolidations test the viability of new or newly integrated systems. With increasing competitive pressures being placed on businesses and the trend towards globalization, companies are engaging more and more in M&A activity. Many companies looking to expand or streamline their business will use investment banks for advice on potential targets and/or buyers. This normally will include a full valuation and recommended tactics. The investment bank's role in mergers and acquisitions falls into one of either two buckets: seller representation or buyer representation (they are also called "target representation" and "acquirer representation"). Valuation: One of the main roles of investment banking in mergers and acquisitions is to establish fair value for the companies involved in the transaction. Investment banks are experts at calculating what a business is worth. They are also able to predict how that worth could be altered (i.e., what happens to the value of a company in a number of different scenarios and what those potential futures would mean financially). Financial models are constructed by investment banks to capture the most important fixed and variable financial components that could influence the overall value of a company. These models, depending upon the proposed transaction, can be extremely complex with special variables being added for special areas (i.e., there are different financial factors to consider in different sectors, countries, and markets when predicting or measuring a company's value). Because of their expertise in business valuation, investment banks can also provide the service of arbitrage opportunities for their clients. For instance, if a bank has performed valuation on a potential target company that suggests its market value (or the value of its shares in the marketplace) is less than what the business is actually worth, it may facilitate a merger or acquisition of this target company for its client that carries with it substantial profit opportunity. Buyers versus Sellers: Investment banks do not just rely on buyers and sellers approaching them. They will also source deals by studying the market themselves and approaching companies with their own strategic ideas (i.e., they might suggest that two companies merge, or that one company acquires, or sells to, another). An investment bank that represents a potential seller has a much greater likelihood of completing a transaction (and therefore being paid) than an investment bank that represents a potential acquirer. This seller representation, also known as "sell-side work," is the type of advisory assignment that is generated by a company when it approaches an investment bank and asks it to find a buyer of either the entire company or part of its assets. Generally speaking, the work involved in finding a buyer includes writing a "Selling Memorandum" (a detailed sales document) and then contacting potential strategic or financial buyers. Banks may engage in risk mitigation techniques: (e.g. collateral, derivatives, netting arrangements and asset securitizations) to optimize their exposure to market risk and credit risk, but which in turn may produce other forms of risk (e.g. legal risk). VI. The New and Growing Risks Vendor Risk Management guidelines are provided by Federal Financial Institution Examination council FFIEC. There are three key elements of risk in vendor relationship, they are: 1. Financial loss: may occur due to Internal fraud. (for example, international misreporting of positions, employee theft, and insider trading on an employeeâ&#x20AC;&#x2122;s own account), External fraud. (for example, robbery, forgery, cheque kiting, and damage from computer hacking), Employment practices and workplace safety.(for example, workers compensation claims, violation of employee health and safety rules, organized labor activities, discrimination claims, and generally liability), Clients, products and business practices.(for example, fiduciary breaches, misuse of confidential customer information, improper trading activities on the bankâ&#x20AC;&#x2122;s account, money laundering, and sale of unauthorized products), Damage to physical assets (for example, terrorism, vandalism, earthquakes, fires and floods), Business disruption and system failure.(for example, hardware and software failures, telecommunication problems, and utility outages), Execution, delivery and process management.(for example: data entry errors, collateral management failures, incomplete legal documentation, and unauthorized access given to client accounts, non-client counterparty mis -performance, and vendor disputes). 2. Loss of customer relation: When the bank system fails, either due to negligence of the vendor or due to error, bank is ultimately held responsible. When the system of other banks works well, and if one bank has a failure, there is possibility for customer relations to fail and the customers migrating to other banks. 3. Time needed to replace vendor should the need arise: When contract with a vendor is not yet over, but relationship between the vendor and the bank get strained, the bank may have to look for another vendor. This cannot be at a short notice, and depends on the site of the bank and the transactions that vendor was undertaking for the bank. If the bank is relatively small, change of vendor can be done without much delay. The same way of the vendor was only doing peripheral duties, change could be easily done. Normally for medium sized bank, change of vendor may take anytime between three to six months.

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VII. Conclusion To conclude, banks should follow a model by which the risks can be assessed. It can assist a bank through a software credit risk appraisal, data base regarding the required information. Banking risks arising through vendor mechanisms can also be addressed. With bank automation and core banking, vendors have come to stay in the banking industry, and banks should find out ways and means of minimizing the risks. VIII. References [1] [2] [3] [4] [5] [6] [7]

www.bis.org www.rbi.org www.sourcingnote.com www.boundless.com www.analyticsindiamag.com www.crisil.com www.idrbt.ac.in

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net COMPETITIVE INTELIGENCE LOOP CONTEXTS IN INSURANCE INDUSTRY 1

Dr. Mohammad Reza Hamidizadeh, 2Dr. Ahmad Roosta, 1Dr. Jalil Lajevardi, 1Moghadaseh Mohamadian 1 Professor, 2 Assistant Professor, 3 Assistant Professor, Shahid Beheshti University, Tehran, Iran. 4 PhD. Candidate, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran ________________________________________________________________________________________ Abstract: The paper’s aim is to modeling the dynamic context and relations of affecting and forming components on competitive Intelligence (CI) context. The research method is based on system dynamics. The insurance industry’s senior experts formed the research population. These loops are the infrastructure of organizational intelligence. The research approach is dynamic modeling. The models have casual aspects. The data collection is based on scientific documents, focus groups and deep interview with scholars in insurance industry. The sampling survey is target one which its size determined by maximum precautions estimation. The design software of looped dynamic models is VENSIM. Keywords: Competitive Intelligence, Competitive Intelligence context, Intelligence Business, feedback loops. ____________________________________________________________________________________ I. Introduction Excessive accumulation of knowledge at competition levels is one of the features of new organizations so that increasing the amount of information in the organizations and the necessity of its use in organizational decision lead to the emergence of a phenomenon called CI in the last two decades. CI is considered as a strategic management tool and one of the fastest development areas of business. It is also considered as an important technique in the creation of competitive advantage [21]. CI, which is also called business intelligence, was developed in the context of business in 1980. Although the main source of it goes back to the decades before 1980, the main idea came from Michael Porter in order to use CI techniques to analyze the rivals and industries. Since the end of the Cold War, CI – once widely used in the military environment – has rapidly infiltrated into business competition [18]. One reason for the emergence of the knowledge is explosion of information that has been developed through increasing information and reflection in the proliferation and dissemination of global commercial database. Another reason for the growth of the knowledge might be the nature of the period we are living in; time for major changes in the political and social globalization, increasing the speed of business, increasing global competition, more invasive competition and rapid technological change [7]. In fact, CI is the ability of a systematic process of collecting and analyzing environmental data and information related to competitors, customers, suppliers, industry and market trends and future behavioral patterns. It is known as the art of attracting and transferring knowledge from outside of an organization and environment according to specific rules and certain regulations to the organization for protecting from competitive threats, identifying and exploiting potential opportunities and building the future [11]. In fact, CI is purposeful and coordinated monitoring of competitor activity and identifying them within the framework of the target market, so it is the process of employing ethical and legal methods to discover, provision and delivery of required information to decision makers who want to enhance their business competitive potential. It can be said in the definition of CI that it is gathering information about the environment and competitors intelligently in order to create and maintain competitive advantage [14]. Intelligent systems monitor the organization’s environment, take the events under their supervision and provide required knowledge. Thus, CI is a systematic process for acquiring the knowledge of rivals, evaluation of them in order to simplifying organizational learning and improving, isolation and targeting them in the context of the industry, market and customers [13]. CI cannot be considered aligned with other CI intelligence is beyond all of them so that business intelligence, knowledge management and some of other knowledge-based systems could be considered in the framework of CI [24]. In fact, CI is a backbone of the foundation of competitive advantage is based on it and the strength of these systems provides sustainable capacity and capabilities for CI [8]. Knowledge management (KM) and business intelligence (BI) are closely related to CI. KM focuses on how you can best manage knowledge within your organization, and externally – to customers of the organization and its stakeholders. Similarly, BI is concerned with improving the way information is taken and shared, in order to make it widely available throughout the organization [19].

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II. Literature Review Table 1: Summary of previous research Researchers

Related Study

Mohn, 1989

How to create competitive intelligence in organizations

Mehri & Hosseini, 2005

Designing the competitive advantage model of automotive industry in Iran

Azvine, et al, 2006

Real time of business intelligence for adaptive entrepreneurship

Dishman & Calof, 2008 Muller, 2009

Competitive intelligence: a multi phase approach to marketing strategies Presentation of competitive intelligence model in South Africa car industry

Results Indices of successful implementation of business intelligence in organizations: identifying management requirements, recruitment of qualified personnel, data collection, applying analytical procedures, formulation of appropriate strategies The dimensions of competitive intelligence: networking capabilities, process-orientation, organizational capabilities such as concentration, learning, creativity and development Application of business intelligence at the strategic level of insurance industry, helping to increase the overall efficiency of the organization and optimizing processes The most important problems in order to develop competitive intelligence in organizations: shortage of skilled labor, insufficient participation of executives and legal and ethical issues The components of competitive intelligence: information technology, pay attention to the organization’s inner and outer processes and management approach to innovation and creativity

III. Research Methodology In the present investigation, library and internet resources including books, articles and English and Persian theses were used in the theoretical principles formulation for the confirmation of CI context variables and creation of scientific framework. Moreover, expert opinions based on focal interview relying on Delphi method according to the researcher made questionnaire was used in the test and development of basic theoretical framework of research model. Eventually, the methods of inferential statistics and system dynamism were used after data collection and summarization. The methods of systems dynamism and related functions such as pulse and step functions and sensitivity analysis using Vensim software were used in the present investigation; moreover, structural equations were used in order to determine the relationships of research model variables and associated tests. The population consisted of insurance industry experts including senior and middle managers of insurance industry in iran. The sampling method of the research is purposeful, and the sample size method based on maximum precautionary estimation was employed in order to determine the sample size of the population considering the limited access to the sample experts. IV. Findings CI context is the readiness within the organization and the organizational infrastructure that is the first step in organizational intelligence. In other words, the implementation of CI process is facilitated in case of availability of CI. Figure 1: The Dynamic casual model of CI context in insurance industry Cross-culture Knowledge

<Employees' Perception of Competitive Advantage>

Financial Resources Communication skills

Considering Competitive Intelligence as a Vital Organizational Requirement

Staff Training

Of the Employees

<Managers' Perception of Competitive Advantage>

Lack of Skilled Human Resource Training Potential of Employees

Employees' Perception of Competitive Advantage

Directors Board Support

Change Resistance Managers' Perception of Competitive Intelligence

Motivating Employees Employees Commitment to the Organization

Training Potential of Managers

Encouraging Employees to Share Data & Information

The components of CI context and their dynamic relations were obtained through previous research context, focus groups and deep interview with scholars in insurance industry, and they were approved by structural equations. The components consisted of manager’s perception of competitive intelligence(MPCI), employee’s

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perception of competitive intelligence(EPCI), staff training, communication skills of the employees, motivating the employees, cross-culture knowledge, employees commitment to the organization, Training potential of employees, Training potential of managers, change resistance, considering CI as a vital organizational requirement, directors board support and encouraging the employees to share information. The casual model of CI context is presented in figure 1. In the description of dynamic loops, the first variable is increased firstly and the impact is shown throughout the system. The director board supports the implementation of CI which has upgraded by increasing in considering CI as a vital organizational requirement. Therefore, the motivation of staff in cooperation in the process of CI increases. Employees with more motivation have more understanding of CI ultimately, and this more understanding leads to considering CI as a vital organizational requirement. Thus, a positive and amplifier feedback loop is created, which the increase in considering CI as a vital organizational requirement has positive effect on this variable. Figure 2 presents the loop. Figure 2: The Dynamic loop of employee perception of CI Cross-culture Knowledge

Relationship Skills of Social Work Among Employees

Financial Resources Considering Competitive Advantage as a Vital Organizational Requirement

Training Employees Lack of Skilled Human Resource Training Potential of Employees

Employees' Perception of Competitive Advantage

Directors Board Support

Change Resistance Motivating Employees

Managers' Perception of Competitive Advantage

Employees Commitment to the Organization

Encouraging Employees to Share Data & Information

Training Potential of Managers

Figure 3: The Dynamic loop of manager perception of CI Cross-culture Knowledge

Relationship Skills of Social Work Among Employees

Financial Resources Considering Competitive Advantage as a Vital Organizational Requirement

Training Employees Lack of Skilled Human Resource Training Potential of Employees

Employees' Perception of Competitive Advantage

Directors Board Support

Change Resistance Managers' Perception of Competitive Advantage

Motivating Employees Employees Commitment to the Organization

Training Potential of Managers

Encouraging Employees to Share Data & Information

In figure 3, the companyâ&#x20AC;&#x2122;s training programs on CI is increased by increasing in the directors board supports from the program of CI within the organization, so the understanding of the employees from CI increases. This

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increased perception comes from interactions with their managers, which the MPCI increases and CI within the organization takes into consideration. Consequently, the directors board supports increases and a positive loop will be created, which the directors board supports has positive impact on the variable. In figure 4, MPCI is increased by increasing in EPCI, and this comes from employees' interaction with their managers. Consequently, more motivation will be emerged in the employees for accepting developed CI programs and the EPCI increases. So, this loop is an amplifier loop too, and this variable has positive impact on itself. In figure 5, the EPCI is increased by increasing in staff training in the field of CI. Employeesâ&#x20AC;&#x2122; more perception of CI leads to the advantages of achieving CI in the organization, increase in considering CI as a vital organizational requirement and attracting support of senior managers from all CI programs. More directors board supports ultimately provide more training for the employees. Figure 4: The dynamic loop of creating motivation in employees Cross-culture Knowledge

Relationship Skills of Social Work Among Employees

Financial Resources Considering Competitive Advantage as a Vital Organizational Requirement

Training Employees Lack of Skilled Human Resource Training Potential of Employees

Employees' Perception of Competitive Advantage

Directors Board Support

Change Resistance Motivating Employees

Managers' Perception of Competitive Advantage

Employees Commitment to the Organization

Encouraging Employees to Share Data & Information

Training Potential of Managers

Figure 5: The Dynamic loop of employees training Cross-culture Knowledge

Relationship Skills of Social Work Among Employees

Financial Resources Considering Competitive Advantage as a Vital Organizational Requirement

Training Employees Lack of Skilled Human Resource Training Potential of Employees

Employees' Perception of Competitive Advantage

Directors Board Support

Change Resistance Managers' Perception of Competitive Advantage

Motivating Employees Employees Commitment to the Organization

Training Potential of Managers

Encouraging Employees to Share Data & Information

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V. Conclusion and Recommendations Studying the loops CI context shows that CI context is considered as context and infrastructure for CI within organizations, which strongly affects CI in the organizations. The loops demonstrates that all variables of CI context have dynamic relations with each other directly and indirectly in a way that variables are affected one after another by change in a variable contentiously, and these impacts return to the changed variable with delay and as a feedback. The present research demonstrates that a director board supports is of key variables among the context variables that almost affects all the variables. It can be said that the development of CI in organizations and the organization’s performance are improved by directors board supports, which is affected by their perception of CI and their approach to CI. Therefore, it is proposed to provide training courses on CI for organizations' senior managers and to justify them about the advantages of CI in achieving sustainable competitive advantage in the organization and to deem CI necessary in the organization. In this regard, increasing the managers' training budget is effective. It is determinant in organization’s CI and achieving sustainable competitive advantage. Moreover, it is proposed to use specialty and personality choices in the recruitment of senior managers, because the CI is affected by managers' ability. In addition, the managers' personality features such as resistance to changes is determinant in the acceptance of CI. Reference [1] [2] [3] [4] [5] [6] [7]. [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

[24] [25] [26] [27] [28]

Alessi, S. M. (2003). Designing educational support in system-dynamics-based interactive learning environments. Simulation & Gaming, 31(2), 178-196. IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services. San Francisco, California, USA, 29-39. Barlas, Y. (2002). System dynamics: Systematic feedback modeling for policy analysis. Encycle opedia Of Life Supports Systems. Barney, J. B. (2001a). Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 6, 643–650. Calof, J., Wright, S. (2008). Competitive intelligence A practitioner, academic and inter-disciplinary perspective, European Journal of Marketing, 42 (7/8) 717-730. Calof, J.L. and Smith, J. (2010). The Integrative Domain of Foresight and Competitive Intelligence and Its Impact on R&D Management. R&D Management, 40(1): 31-39. Chen, H., Chau, M., & Zeng, D. (2002). CI Spider: a tool for competitive intelligence on the Web. Decision Support Systems, 34 , 1–17. Dishman, P. and Calof, J. (2008), “Competitive intelligence: a multiphasic precedent to marketing strategy”. European Journal of Marketing, 42, (7/8), 766-785. Dishman, p., Pearson, T. (2003), “assessing intelligence ass learning within an industrial marking group: a pilot study”, industrial marketing management, 32, 615-620. DuToit, A. & Muller, M.L. (2004). ‘Organizational structure of competitive intelligence activities: a South African case study’, South African Journal of Information Management 6(3), 308-321. Fleisher, C.S. (2008), “Using open source data in developing competitive and marketing intelligence”, European Journal of Marketing, 42 (7/8), 852-866. Hamidizadeh, Mohammad Reza (2014). ” System Dynamics”, Tehan, Shahid Beheshti University Publishing. Johns, P., Doris, Doren, C. V. (2010),"Competitive intelligence in service marketing: A new approach with practical application", Marketing Intelligence & Planning, Vol. 28 Iss: 5 pp. 551 – 570. Mc Gonagle, J.J. and Vella, C.M. (2008), The Intelligence Age of Competitive Intelligence, Greenwood Publishing Group, Inc., Westport, CT. Mohn, N. Carroll (1989) "How To Create a Corporate Competitive Intelligence System", American Journal of Business, Vol. 4 Iss: 2, pp.3 – 6 Muller, M. L. (2009), “How and what others are doing in competitive intelligence: outsourcing model”, Competitive Intelligence, 11(2). Murphy, Ch. (2005), “Competitive Intelligence, Gathering, Analyzing and putting it to work”, Published by Gower Publishing Limited. Pellissier, R. & Nenzhele, T.E., (2013), ‘Towards a universal competitive intelligence process model’, SA Journal of Information Management, 15(2), 567-574. Peterisor, Ioan, Strain, Natalia. (2013). Approaches On The Competitive Intelligence. The USV Annalas Of Economics and Public Adminstration, 13(17), 100. Porter, M. (1985); “Competitive Advant, age: Creating and Sustaining Superior Performance “, New York: free Press. Qiu, T. (2008), “Scanning for competitive intelligence: a managerial perspective, European Journal of Marketing, Vol. 42 No. 7/8, pp. 814-35. Rouch, D., and Santi, P. (2001) “competitive intelligence adds Value”, Eroupean management journal, 19(5), PP. 552-559. Saayman, Andrea.; Pienaar, Jaco.; de Pelsmacker, Patrick.; Viviers, Wilma.; Cuyvers, Ludo.; Muller, Marie-Luce., & Jegers, Marc.(2009). Competitive intelligence: construct exploration, validation and equivalence. Aslib Proceedings: New Information Perspectives, 60(4), Pp: 383-411. Tanev, S., Bailetti, T. (2008), “Competitive intelligence information and innovation in small Canadian firms”, European Journal of Marketing, Vol. 42 No. 7/8, pp. 786-803. Toit, A. (2003); “Competitive Intelligence in the knowledge Economy”, International Journal of Information Management, 23, PP. 111-120. Vivers, W.; Muller, M.L. & Du Toit, A.S.A. (2005). Competitive Intelligence: an instrument to enhance competitiveness in South Africa. South African journal of economic and management science, 8(2):246-254. Wright, S., Clofe, J. L. (2006). The Quest for Competitive, Bussiness and Marketing Intelligence. European Journal of Marketing, 40(5/6), PP. 453-465. Zangoueinezhad, A., Moshabaki, A.( 2009). The role of structural capital on competitive intelligence. Industrial Management & Data Systems, Vol. 109 Iss: 2 pp. 262 – 280.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net TALENT MANAGEMENT IN TCS Dr. Namita Rath1, Ms. Sujata Rath2 Asst. Prof., GIFT, Bhubaneswar, Odisha, India 2 Asst. Prof., AMITY Global Business School, Bhubaneswar, Odisha, India _____________________________________________________________________________________ Abstract: Talent Management has become one of the most important buzzwords in Corporate HR and Training today. Right talent is the greatest asset for any enterprise and one of the essential roles of HR is to make sure that the employees with the right skills stick with the company for long enough. The issue with many companies today is that their organizations put tremendous effort into attracting employees to their company, but spend little time into retaining and developing talent. One of the most important factors that go into IT business is human capital, and therefore, sustainability of revenue growth in the IT Services industry is directly dependent on the organization’s ability to attract the right talent and thereafter, up-skilling, motivating and retaining them in the organization. This paper brings out the Talent Management Strategy of Tata Consultancy Services (TCS), one of the leading software companies in India which is viewed as a benchmark company in the industry for talent nurture and retention. Key Words: Talent Management, Human Capital, Employee Value Propositions _________________________________________________________________________________________ 1

I. Introduction Today, know-how, innovation and experience constitute the main advantages of an organization over its competitors. The talented employees drive these advantages. Hence, attracting, developing and retaining talent is fundamental to every company to remain ahead in the competition. The issue with many companies today is that their organizations put tremendous effort into attracting employees to their company, but spend little time into retaining and developing talent. Research on the value of talent management consistently uncovers benefits in the critical areas like - revenue, customer satisfaction, quality, productivity, cost and market capitalization. Tata Consultancy Services (TCS), one of the leading software companies has adopted a scientific and innovative approach for harnessing talent management. The company has been investing more than 6 per cent of its annual revenues in training, learning and development. II. Talent Management The term “Talent Management” was coined by McKinsey & Company following a 1997 study. Talent consists of those individuals who can make a difference to organizational performance either through their immediate contribution or, in the longer-term, by demonstrating the highest levels of potential. Talent management is the systematic attraction, identification, development, engagement, retention and deployment of those individuals who are of particular value to an organization, either in view of their ‘high potential’ for the future or because they are fulfilling business/operation-critical roles. Thus, it is a conscious, deliberate approach undertaken to attract, develop and retain people with the aptitude and abilities to meet current and future organizational needs. Talent Management is simply a matter of anticipating the need for human capital and then setting out a plan to meet it. It often times referred to as Human Capital Management, is the process of recruiting, managing, assessing, developing and maintaining an organization’s most important resource - people. Right talent is the greatest asset for any enterprise. In fact, one of the most important roles of HR is to make sure employees with the right skills stick with the company for long enough. Talent management (TM) brings together a number of important human resources (HR) and management initiatives. Quite often, organizations adopting a TM approach focus on co-coordinating and integrating:  Recruitment - ensuring the right people are attracted to the organization.  Retention - developing and implementing practices that reward and support employees.  Employee development - ensuring continuous informal and formal learning and development.  Leadership and "high potential employee" development - specific development programs for existing and future leaders.  Performance management - specific processes that nurture and support performance, including feedback/measurement.  Workforce planning - planning for business and general changes, including the older workforce and current/future skills shortages.  Culture - development of a positive, progressive and high performance "way of operating".

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Successful talent management is not just about recruiting, retaining and developing a high-performing workforce but also aligning their talent management practices with their culture, values and strategic goals and the integration of these programmers across the entire organizations. An important step in talent management is to identify the employees (people and positions) that are critical to the organization. At present, the skill shortages and the ageing workforce are also helping organizations to focus on the talent management. Many leading companies have decided to develop their own people, rather than trying to hire fully skilled workers. In summary, every organization should be implementing talent management principles and approaches. III. Literature Review Workplaces everywhere are obsessed with employee engagement. Engaged workers are more productive, perform better, motivate others and, perhaps most importantly – stay. So, it is also no surprise that in a labor market such as India where attrition rates of 20-30 % are normal and 50 % in industries such as IT not unheard of, serious questions about engagement are being asked (Smith, 2012). A recent Mercer survey highlights that no fewer than 54 % of Indian workers are seriously considering leaving their jobs, and that figure spikes to 66 % in the 16-24 year age bracket. It is interesting to note that approximately 75 % of the IT personnel are younger than age 45. Many managers in the IT industries are in their 20’s and 30’s therefore making it more difficult to manage and retain them (Shayo, 2004). According to LBW Consulting (Leadership in Business Worldwide), the cost of replacing an employee ranges from 29 % (non-management) to 46 % (management) of the person's annual salary. Expenses are also incurred when someone else does the person's job in the interim, leading to a domino effect on employee cost. (Singh, 2012) Good employees want to develop new knowledge and skills in order to improve their value in the marketplace and enhance their own self-esteem. (Irwin, 2011) Therefore, it is wise to plan the first initial days of his work and train him for the work he is going to perform in the office, rather than leaving him on his own. Leading employers see this not as a cost but an opportunity to both harness worker potential as well as a talent attraction and retention lever (Smith, 2012). IV. Talent Management in TCS The human resources strategy of Tata Consultancy Services (TCS), the largest provider of information technology (IT) and business process outsourcing (BPO) services in India has enabled the Company to attract, integrate, develop and retain the best talent required for driving business growth. The sustained strategic focus to enhance employee capability, improve efficiency and groom future leaders has helped TCS to maintain its benchmark status in the IT industry. As on March 31, 2013 the Company employed 2,76,196 associates representing 118 nationalities deployed across 55 countries. The 'workforce management strategy' is executed optimally to fulfill business demand, deliver consistently high utilization rates and keep manpower costs within the desired range. The Company has created a performance driven environment where innovation is encouraged, performance is recognized and employees are motivated to realize their potential. Its relentless pursuit to connect with employees on a regular basis, communicate in an open and transparent manner, provide opportunities to learn and grow within the organization have yielded desired results as is evident from the high retention rates and the motivation and engagement levels of its employees. The attrition rate at TCS in 2006 was 10.6 % which was the lowest in the Indian software and IT industry. As TCS continued to expand globally, it faced the challenge of grooming and retaining a diversified talent pool. Also with rising manpower requirement, TCS increasingly hired non-technical science graduates, which posed a challenge to groom and bring them on a common platform. A small experiment called “Ignite” was started in December 2006 to strengthen the company’s talent base. It was launched to hire, train and deploy two batches of trainees from non-technical science background. Initially, TCS recruited engineers to meet its needs at the business front. But, later on to expand and increase its scalability, the company felt the need to have a more diverse team in terms of intellectual, social and culture context. In February 2009, TCS changed its hiring strategy and started focusing on just-in-time hiring or realtime talent management. To build a quality talent pool, TCS started a programme called Academic Interface Programme (AIP). Also, various kinds of training programmes were conducted at TCS - Learning and Development, Initial Learning Programmme, Continuous Learning Programme, Leadership Development Programme, Foreign Language Initiative and Workplace Learning. Compensation management system at TCS is based on the economic value added (EVA) model. The company conducts appraisal of its regular employees twice in a year, and also at the end of the project in case of employees hired specifically for various projects. In order to identify its outstanding talent, TCS has been recognizing the contribution of its people in many ways. In 1997, TCS set up a state-of-the-art training centre, ‘Technopark’ at Thiruvananthapuram which offers training to new recruits and TCS staffers at various levels. ‘Technopark’ provided the employees with three

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kinds of training programmes - technology, attitudes and management. TCS has a Manpower Allocation Task Committee (MATC) which determined the career path for employees. V. Learning and Development The Company continued to invest in enhancing its human capital through building skills and competencies for its associates. It has brought a paradigm shift in the learning process which is called the next-gen learning & development (L&D). The learning eco-system has been transformed by investing in interactive classrooms, video based training and social media enabled social learning. 'Any Time-Any Where' learning has become a reality in TCS. The new recruits from colleges are trained to be IT professionals through its elaborate initial learning programme. It has expanded its training infrastructure capacity by building a state of the art training center in Thiruvananthapuram with a capacity to accommodate 15,000 trainees. VI. Talent Management, Leadership Development and Talent Retention The performance and career management processes of TCS are fully globalised. Digitized systems have been enhanced and new 'Career Hub' has been launched for streamlining the process of recording aspirations, identifying high potentials, mentoring and tracking career movement of employees. The culture of reward and recognition in TCS is aided by 'TCS Gems', the global reward and recognition tool, with well-defined criteria and processes to enhance performance. The Company participated in multiple compensation surveys in India and other geographies to ensure that its compensation and rewards strategy remains competitive. A healthy leadership pipeline is maintained through the layered framework of' Leadership Development Programmes (LDP), focusing on developing behavioural, business and people competencies. Potential leaders are identified and are nurtured through LDPs, and given challenging roles to build leadership capability. TCS has tied up with globally recognized coaching agencies to provide coaching intervention to its leaders. The organization culture of open communication is supported through a highly flexible and transparent internal social networking platform. It empowers employees to articulate their feelings freely, exchange ideas and contribute to the organizational growth. In addition to this, regular connect with the employees helps to understand the pulse of the organization and take appropriate measures to keep the workforce engaged and motivated. A number of non-work related employee engagement initiatives such as fun events, sports, cultural activities and volunteering for social causes are organized across the globe under its employee engagement platform known as 'Maitree'. The culture of volunteering helps employee bonding within the organization and reduces stress at work. Employees are also encouraged to involve their families in these activities. Employee health and safety are of crucial importance. Fit4Life, health awareness sessions, periodic medical check-ups, gymnasiums in offices and 24x7 'Employee Assistance Programme' are some of the important initiatives undertaken by the Company to encourage health consciousness. In fact, the employee engagement initiatives and various HR interventions have helped the company to control attrition. Today, TCS remains the industry benchmark for talent retention. Its attrition rate including BPO has come down to 10.60 % in 2013, as compared to 12.20 % in 2012. VII. Talent Diversity The Company employed persons from 118 different nationalities. The number of non-Indian nationals was 21,282 as at March 31, 2013 (17,329 as of March 31, 2012). Efforts are made continuously to integrate differently-abled individuals into the workforce. Efforts are also made to increase recruitment of individuals belonging to disadvantaged sections of society. TCS proactively creates an environment of inclusion to attract and retain women. Women constituted 32.40 % of the Company's workforce as on March 31, 2013 (31.60 % as on March 31, 2012). Its progressive policies and customized programmes such as executive education programme for women in mid-management, interactive forums and women discussion circles address the aspirations and needs of its women employees. Special initiatives are also taken to strengthen cultural orientation of employees and help drive "One TCS Culture" across the organization. A learning module has been created specifically for managers to enable them to work with diverse teams. Employee Engagement: Employee retention and motivation are greatly facilitated through closer engagement with employees and by fostering a spirit of community, through shared activities beyond work. The company actively supports athletic and sporting activities at the national, regional and local level and encourages employees to participate. On TCS campuses, the focus on wellness translates into diverse activities including yoga, aerobics, tennis, badminton coaching, cricket and football tournaments. TCS Maitree, founded in February 2002, strives to create a spirit of camaraderie among TCS associates and their families by organizing social activities and events. TCS has grown tenfold in the last few years with

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associates working in several locations around the world. TCS Maitree encourages associates and their families to look upon themselves as a part of TCS’ extended family. The initiatives undertaken through TCS Maitree cultivate and propagate volunteer-driven, meaningful activities for associates and their families. Human Resources within TCS leads, directs and facilitates all such associate engagement activities and programmes related to Corporate Sustainability. Human Capital Development and Sustenance The largest factor that goes into IT business is human capital and therefore, sustainability of revenue growth in the IT Services industry is directly dependent on the organization’s ability to attract the right talent in the right quantity and thereafter, up-skilling, motivating and retaining them in the organization. TCS’s highly educated and technically sound workforce is highly valued globally. Recognizing the criticality of human capital to the business and its sustainability, TCS has mature processes devoted to attracting, retaining and developing this human capital, assessed at PCMM Level 5. A critical component of sustaining TCS’ growth trajectory has been the company’s ability to attract talent in sufficient numbers and to successfully scale up the talent acquisition process. Experienced professionals are recruited through online jobsites, head-hunters, recruitment agencies and through employee reference schemes. Other sources of experienced talent include strategic initiatives like M&A and In-sourcing. On-campus recruitment of fresh engineering graduates plays a very significant part in TCS’ talent acquisition strategy, so the company is invested in building strong relationships with universities across the world and in improving the quality of academic training at engineering colleges through Faculty Development Programmes, Student Workshops and Project Support and Internships. TCS maintains a web portal linked to the TCS website for continuous dialogue with academia on the performance of their alumni and on the curriculum, with useful resources for students deciding on their career. In addition, TCS hosts “Sangam”, which is an annual meet with academic leaders from major engineering and management institutes. IT Industry being totally dependent on its employees, the employees should be given the first priority among all the factors of production. Talent retention is another critical success factor – as important as talent acquisition – and TCS has invested heavily in building an employee-friendly culture and developing mature people-processes toward improving it. TCS has been assessed enterprise-wide at PCMM Level 5 and its retention programs are considered the best-in-class in the industry. Its career development, training, and rewards and recognition programs ensure continued motivation for its associates. VIII. Conclusion Today, the concept of Talent Management continues to be adopted as more companies come to realize that their employees’ talents and skills drive their business success. In addition to this, it has come to be established that employee retention is more cost effective than hiring. As such, in order to support its key objectives TCS has aligned talent management with business strategy, which has helped to nurture talent and retain it. It is evident from the fact that the company's attrition rate has come down to 10.60 % in fiscal 2013, as compared to 12.20 % in fiscal 2012 and is the lowest in the Indian software and IT industry. In this context it is worthwhile to recall Andrew Carnegie, famous industrialist of 19th century who is known for having built one of the most powerful and influential corporations in US - “Take away my factories, my plants; take away my railroads, my ships, my transportation, take away my money; strip me of all of these but leave me my key people, and in two or three years, I will have them all again.” References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16].

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