igi global proof

IJPMAT Editorial Board Editor-in-Chief:

Debaprosanna Nandy, The Institute of Cost Accountants of India, India

International Advisory Board: Gholam R. Amin, Sultan Qaboos U., Oman Anil Bhuimali, U. of North Bengal, India Supachet Chansarn, Bangkok U., Thailand Subhash Datta, NIILM Centre for Management Studies, India Kristof De Witte, U. of Maastricht, Netherlands Ali Emrouznejad, Aston U., UK François Pinet, Cemagref - Clermont Ferrand, France Associate Editors:

Shounak Roy Chowdhury, Jindal Global Business School, India Ranjan Kr. Gupta, West Bengal State U., India

IGI Editorial:

Heather A. Probst, Senior Editorial Director Jamie M. Wilson, Managing Editor Adam Bond, Editorial Assistant Chris Hrobak, Journal Production Manager Christen Croley, Journal Production Assistant

International Editorial Review Board: Panayiotis Curtis, Technological Institute of Chalkis, Greece Debasis Dasgupta, West Bengal Industrial Infrastructure Development Corporation, India M.P. Hanias, National and Kapodistrian U. of Athens, Greece

L. Magafas, Kavala Institute of Technology, Greece Jibendu Kumar Mantri, North Orissa U., India Ramanjeet Singh, Institute of Management & Technology, India

IGI GLOBAL PROOF

IGIP

IGI Publishing www.igi-global.com

CALL FOR ARTICLES International Journal of Productivity Management and Assessment Technologies An official publication of the Information Resources Management Association The Editor-in-Chief of the International Journal of Productivity Management and Assessment Technologies (IJPMAT) invites authors to submit manuscripts for consideration in this scholarly journal.

M ission

The mission of the International Journal of Productivity Management and Assessment Technologies (IJPMAT) is to develop, promote and disseminate knowledge in the areas of productivity, efficiency, and performance management. The measurement of organizational productivity, performance, and efficiency is an essential part of change and contributes to general welfare of organizations and, to a larger extent, societies. By measuring productivity and efficiency, it is possible to evaluate the performance of an organization by comparing it with benchmarks of international best practices. Thus, from a societal point of view, productivity management is of high importance and value. The international dimension of the journal is emphasized to overcome cultural and national barriers and meet the needs of accelerating technology and changes in the global economy. Novel and fundamental theories, algorithms, technologies, and applications support this mission.

Coverage • • • • • • • • • • • • • • • • • • •

Artificial neural networks Balanced score card Benchmarking Business intelligence model Business process re-engineering Comparative methodology and study Data envelopment analysis Data mining Enterprise resource planning Environmental productivity, efficiency and performance analysis Financial statement analysis Human productivity and performance analysis Innovation management Labor productivity Malmquist index Mergers and acquisitions Modeling techniques Multi-criteria decision making Operations management and strategy

ISSN 2160-9837 eISSN 2160-9845 Published quarterly

IGI GLOBAL PROOF • • • • • • • • • • • • •

Outsourcing Performance benchmarking and ranking Productivity and performance management Quality and business excellence management Sensitivity analysis Six sigma Statistical quality control Stochastic frontier analysis Strategic alliances Supply chain management Technological change and its impact Total productivity management Total quality management

All submissions should be e-mailed to: Debaprosanna Nandy, Editor-in-Chief, E-mail: [email protected]

Ideas for Special Theme Issues may be submitted to the Editor-in-Chief.

Please recommend this publication to your librarian. For a convenient easy-to-use library recommendation form, please visit: http://www.igi-global.com/ijpmat

International Journal of Productivity Management and Assessment Technologies January-March 2012, Vol. 1, No. 1

Table of Contents

Editorial Preface

i

Debaprosanna Nandy, The Institute of Cost Accountants of India, India

1

Research Articles

Efficiency Study on Proposed Merger Plan of State Bank of India (SBI) and its Subsidiaries: A DEA Perspective Debaprosanna Nandy, The Institute of Cost Accountants of India, India Manas Kr. Baidya, Malda College, India

18 A Study on the Contribution of 12 Key-Factors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by Using a Neural Network Model E. Stathakis, Kavala Institute of Technology, Greece M. Hanias, University of Athens, Greece P. Antoniades, Kavala Institute of Technology, Greece L. Magafas, Kavala Institute of Technology, Greece D. Bandekas, Kavala Institute of Technology, Greece

IGI GLOBAL PROOF

29 Business Process Models Representation by Deducing Interpretative Evidences on Intuitively Common Symbols Saleh Alwahaish, VŠB–Technical University of Ostrava, Czech Republic Ahmad Jaffar, United Arab Emirates University, UAE Ivo Vondrák, VŠB–Technical University of Ostrava, Czech Republic Václav Snášel, VŠB–Technical University of Ostrava, Czech Republic 40 Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method Golam Kabir, University of British Columbia, Canada Razia Sultana Sumi, Stamford University Bangladesh, Bangladesh

i

Editorial Preface Debaprosanna Nandy, The Institute of Cost Accountants of India, India

The main objectives of the International Journal of Productivity Management and Assessment Technologies (IJPMAT) are to develop, promote and disseminate knowledge in the areas of productivity, efficiency, and performance management. The measurement of organizational productivity, performance, and efficiency is an essential part of change and contributes to general welfare of organizations and, to a larger extent, societies. By measuring productivity and efficiency, it is possible to evaluate the performance of an organization by comparing it with benchmarks of international best practices. Thus, from a societal point of view, productivity management is of high importance and value. The international dimension of the journal is emphasized to overcome cultural and national barriers and meet the needs of accelerating technology and changes in the global economy. Novel and fundamental theories, algorithms, technologies, and applications support this mission. This first issue aims to develop and promote an international discussion forum for academicians, professionals and practitioners working and interested in research and practice of various fields of productivity, efficiency and performance measurement and management for different organizations.

The paper “Efficiency Study on Proposed Merger Plan of State Bank of India (SBI) and its Subsidiaries: A DEA Perspective” by Debaprosanna Nandy and Manas Kr. Baidya address that the Indian Banking industry is undergoing unprecedented changes driven by consolidation through mergers and acquisitions like rest of the world. Merger of State Bank of India (SBI) and its subsidiary banks have been considered for several years. SBI has already merged State Bank of Saurashtra and State Bank of Indore with itself. SBI management proposes to merge its five remaining subsidiaries with itself in the next one or two fiscal years. This paper measures and examines technical efficiency of SBI and its subsidiaries before and after their hypothetical merger. For this, the study has utilized the two basic DEA models – CCR and BCC to measure technical efficiencies of selected major Indian commercial banks before and after merger of SBI and its associates. “A Study on the Contribution of 12 Keyfactors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by using a Neural Network Model” by E. Stathakis, M. Hanias, P. Antoniades, L. Magafas, and D. Bandekas shows a new methodological framework regarding the measuring of the contribution of some key-factors on the regional growth

IGI GLOBAL PROOF

ii

rate and forecasting the future development rates, based on Neural Network Models (NN Models). It is a serious attempt to be studied the contribution of twelve key-factors to the change of the Regional Gross Domestic Product of the Region of East Macedonia -Thrace during a long-term of growth process, by creating and using a suitable Neural Network Model. More specifically, twelve key-factors are studied for the first time, in order to be investigated, scientifically, firstly their percentage contribution to growth of the regional economy and secondly, after that, to be predicted how much the RGDPunder certain conditions-will be changed. In other words, also for the first time, it is used a NN Model with inputs the twelve key-factors in order to be evaluated and measured, at the best precise, their percentage contribution to the RGDP. The final results show that our NN Model is applicable to our well-adapted data and we conclude that the most important factors-as growth drivers- are “Gross Business Expenses for R&D expressed as % of RGDP,” “Domestic Consumption of electricity” and “Industrial Production Index. The novelty of the approach lies, first in linking this analysis to the broader issue of regional growth, second in using a great number of determinants influence the regional growth. The model seems to be useful to all stakeholders, since it can be used, after the necessity adaptations, for any region that pursues sustainable growth, through suitable growth policies. Saleh Alwahaishi, Ahmad Jaffar, Ivo Vondrák, and Václav Snášel in their research article “Business Process Models Representation by Deducing Interpretative Evidences on Intuitively Common Symbols” mention that through quantitative analysis, previous researchers had proven a significant preference towards a specific set of notations for modeling business processes. The drawn conclusion revealed a significantly correlated coefficient preference to Norm Process Chart for using easily recognizable symbols to intuitively elicit clear understanding in representing business process models. Further interpretative analysis to qualitatively enhance these findings will only

prove and strengthen the above claimed beyond reasonable doubt. The approach is to measure respondent level of accuracy in interpreting different case studies modeled using three different modeling techniques shown to respondents in different randomized sequences. The analysis includes correlating the finding against the time taken as well as respondents’ level of confidence in interpreting these models. The significantly correlated results again confirmed beyond reasonable doubt Norm Process Chart being respondents ultimate choice. Further comparative analysis between results from an earlier investigation against the latter, revealed similar patterns in respondents’ responses despite respondents dispersed ethnicity and educational backgrounds. The paper “Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method” by Golam Kabir and Razia Sultana Sumi attempts to evaluate and select the concrete production facility location, which is an important strategic decision making problem for both public and private sector. The multi-dimensional, multi-criteria nature of the concrete production facility location problem limits the usefulness of any particular single objective model. In this study, social, economical, technological, environmental and transportation factors and sub criteria, have been derived to make the optimal concrete production facility location selection decision more realistic and effectual. In this paper, an improved and more appropriate concrete production facility location evaluation and selection model has been developed by integrating Modified Delphi and Fuzzy Analytic Hierarchy Process (FAHP) with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. A numerical example is presented to show applicability and performance of the proposed methodology followed by a sensitivity analysis to discuss and explain the results. I hope the papers selected for this inaugural issue will be a source of useful results for productivity management and its assessment using different modern tools and technologies for different organizations and provide a

IGI GLOBAL PROOF

iii

direction for future research in this area. I am grateful for the valuable contributions offered by the authors, associate editors, editorial review board members, international advisory board members and researchers across the globe for the success of the inaugural issue of IJPMAT. The valuable support offered by Ms. Heather Probst, Ms. Jamie Wilson, and their teammates of IGI Global for this inaugural issue is highly

appreciated. I would like to invite the researchers across the globe to submit their research papers for the future issues of IJPMAT. Debaprosanna Nandy Editor-in-Chief IJPMAT

Debaprosanna Nandy obtained his PhD in Economics as UGC Fellow from the University of North Bengal, India. He is a Post-graduate in Commerce from Calcutta University after being graduated in Commerce (Hons.) from Goenka College of Commerce & Business Administration, Kolkata, India. His MBA is from the Indian Institute of Social Welfare & Business Management, Kolkata, India. He is a Fellow member of The Institute of Cost Accountants of India and Institution of Valuers, India. He is also a Member of the Operational Research Society, UK, and Indian Public Health Association. He was qualified successfully in State Level Eligibility Test for Lectureship and West Bengal Audits & Accounts Examination. He has vast experience in industry as well as in teaching and research. At present he is working as Director (Research & Journal) at The Institute of Cost Accountants of India. He has previously worked as Associate Professor of Finance, CIMA Course Leader & Faculty-in-Charge of AMP (Banking & Finance) at Symbiosis International University, Pune, India. He has taught in several management institutes as visiting faculty of Applied Finance, Performance Management, Valuation Management, Financial Management, International Financial Management, Management Accounting, Cost Accounting, Security Analysis & Portfolio Management, Accounting and Taxation. He was the Chairman of Siliguri-Gangtok Chapter of Cost Accountants, ICWAI, India. He is closely associated with the Confederation of Indian Industry and other renowned institutions. He is the Editor-in-Chief as well as the editorial board member of several reputed international journals. He has contributed in many national and international journals and also attended quite a large number of national and international conferences. He has conducted successfully several MDP, EDP, FDP, conferences and workshops. He has written several books on Management, Economics, Business Communication, Entrepreneurship Development and Performance Management. His present area of interest is performance management of different organizations.

IGI GLOBAL PROOF

International Journal of Productivity Management and Assessment Technologies, 1(1), 1-17, January-March 2012 1

Efficiency Study on Proposed Merger Plan of State Bank of India (SBI) and its Subsidiaries: A DEA Perspective

Debaprosanna Nandy, The Institute of Cost Accountants of India, India Manas Kr. Baidya, Malda College, India

ABSTRACT The Banking industry is undergoing unprecedented changes driven by consolidation through mergers and acquisitions all over the world. India is no exception. Merger of State Bank of India (SBI) and its subsidiary banks have been for several years, and SBI has already merged State Bank of Saurashtra (2008) and State Bank of Indore (2010) with itself. SBI management proposes to merge its five remaining subsidiaries within the next two fiscal years. The present paper measures and examines technical efficiency of SBI and its subsidiaries before and after their hypothetical merger. The study has utilized the two basic DEA models – CCR (Charnes, Cooper and Rhodes) and BCC (Bankers, Charnes and Cooper) to measure technical efficiencies of selected major Indian commercial banks before and after merger of SBI and its associates for the financial year 2009-10.

IGI GLOBAL PROOF

Keywords:

Bank Mergers, Banking Industry, Data Envelopment Analysis (DEA), Peer Banks, Relative Technical Efficiency

1. INTRODUCTION Banking industry is undergoing unprecedented changes driven by consolidation by means of mergers and acquisitions all over the world. One of the principle objectives behind the merger and acquisitions in banking sector is to reap the benefits of economies of scale. Moreover, mergers and acquisitions in the banking industry have resulted in large universal banks in terms of total assets, products, and geographical diver-

DOI: 10.4018/ijpmat.2012010101

sification. Large banks are in a better position to introduce technology and reduce cost than small banks. Large banks have normally large capital and easily utilize economies of scope. For all these reasons, size of banks is considered to be normally associated with higher efficiency. Bank size thus is now an important factor in today’s banking efficiency analysis study. However, literature on banking efficiency does not suggest a consistent relationship between size and efficiency, which is the area of interest for researchers like us. Merger of State Bank of India (SBI) with its subsidiary banks has been considered for

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

2 International Journal of Productivity Management and Assessment Technologies, 1(1), 1-17, January-March 2012

several years. SBI management has got a blanket approval from the government to merge all the banks. SBI management has taken this consolidation exercise step by step. The first step towards unification occurred on 13 August 2008 when State Bank of Saurastra merged with SBI, then on 19 June 2009, the SBI board approved the merger of its subsidiary, State Bank of Indore, with itself. SBI proposes to merge its five remaining subsidiaries - State Bank of Hyderabad, State Bank of Patiala, State Bank of Bikaner and Jaipur, State Bank of Travancore and State Bank of Mysore with itself by mid 2012 (“SBI to take a view on merger,” 2011). This consolidation is aimed at making the State bank group a stronger and more resilient organisation. Merger/acquisition of its subsidiary banks by SBI should not be seen as a merger in the conventional sense but is more in the nature of restructuring within the Group as SBI already held 75% or more equity stake in all its subsidiary banks. Parliamentary Standing Committee on Finance reported while SBI has also stepped up its efforts to grow organically, the inorganic growth through mergers would also help the bank in scaling up within an acceptable time frame, to enable it to compete on an equal footing with foreign banks, not only in India but in the international economic arena as well (Feb 23, 2011; Business Line). On the other side, State Banks’ Staff Union and SBI Officers’ Association opposed this proposed merger of associate banks with State Bank of India and termed this as a retrograde step. In this background, an obvious question arises in our mind – ‘will the merger proposal of SBI and its subsidiaries produce an efficient bank in Indian banking industry?’Therefore, the present study evaluates efficiencies of Indian banks particularly seven banks of SBI associate before and after hypothetical merger of such seven banks using data envelopment analysis (DEA). For this, the study has utilized the two basic DEA models – CCR and BCC to measure technical efficiencies of 31 major Indian commercial banks (before merger) and 25 banks (after merger) taking hypothetically merged SBI

in place of seven SBI group banks based on the financial data of the financial year 2009-10. The main objectives of this paper are• •

To measure the extent of relative technical efficiency of hypothetically merged SBI. To analyse its efficiency from various perspectives of DEA methodology.

2. MERGER—INDIAN BANKING INDUSTRY The Indian banking industry has been in the process of transformation and consolidation ever since 1961. The Banking Regulation Act, 1949 empowers the regulator with the approval of the government to amalgamate weak banks with stronger ones. But, the thrust on consolidation has emerged with the Narasimham committee (1991) emphasising on convergence and consolidation to make the size of Indian commercial banks comparable with those of globally active banks. Further, the second Narasimham Committee (1998) had also suggested mergers among strong banks, both in the public and private sectors and even with financial institutions and Non-Banking Finance Companies (NBFCs). The Second Narasimham Committee - 1998 recommended a multi-tier banking system with existing banks to merge into 3-4 international banks at the topmost level, 8-10 national banks engaged in universal banking at the next level and local and rural banks confined to specific regions (Bansal et al., 2011). Deregulation in the financial market, market liberalization, economic reforms, pressure from global competition, development of technology, Basel-II implementation and a number of other factors have played an important function behind the growth of mergers and acquisitions in the Indian banking sector. Nevertheless, there are many challenges that are still to be overcome through appropriate measures. The mergers and acquisitions in the banking sector of India are overseen by the Reserve Bank of India (RBI). Merger schemes in Indian banks can be classified as - Forced

IGI GLOBAL PROOF



merger, Voluntary mergers, Universal Banking model and Integration of financial services, Alignment of operations of foreign banks with global trends, Merger of Cooperatives, RRBs, and UCB. The motives behind M&A in general in India are market developer, growth & diversification, synergy, reducing bankruptcy risk, economies of scale, economies of scope, strategic integration. However, the motives behind the merger of SBI and its subsidiaries are to bring in economies of scale, reduce administrative overheads, redeploy and channelize trained manpower to business development and, in the process, also reduce avoidable competition from different arms of the same group. From the list of mergers in India given in Table 1, it is observed that prior to 1999, majority of the mergers in India have been primarily triggered to safeguard depositors’ interest and to protect the financial system, whereas in the post-1999 period, there have also been mergers between healthy banks driven by business and commercial considerations. Thus, the new generation mergers on the lines proposed by the Narasimham Committee are a recent phenomenon in the country. Recently, consolidation by M&A in Indian banking system is undertaken as an avenue of inorganic growth.

review on both these issues. Hence we review some of the important studies. In the first issue identified above, Pilloff and Santomero (1997) reported that most studies fail to find a positive relationship between merger activity and gains in either performance or stockholder wealth. But studies by Cornett and Tehranian (1992), Hawawini and Swary (1990), Hannan and Wolkan (1989), and Neely (1987) report a positive reaction in the stock prices of target banks and a negative reaction in the stock prices of bidding banks to merger announcements. A recent study by Chong et al. (2006) on mergers of Malaysian banks shows that forced mergers have destroyed wealth of acquired banks. On the second issue of the bank merger literature, Vennet (1996) studied the impact of mergers on the efficiency of European Union banking industry by using some key financial ratios and stochastic frontier analysis for the period 1988-93 and found that merger improve the efficiency of participating banks. Akhavein et al. (1997) examined the price and efficiency effect of mega mergers on US banking industry and found that banks have experienced higher level of profit efficiency than during post merger period. Berger (1999) found very little improvement in efficiency for merger and acquisition of either large or small banks. Cornett and Tehranian (1992) and Spindit and Tarhan (1992) provided evidence for increase in post-merger operating performance. But the studies of Berger and Humphrey (1992), Piloff (1996), and Berger (1997) do not find any evidence in post-merger operating performance. Some other type of studies examined the potential benefits and scale economies of mergers. Landerman (2000) explored potential diversification benefits to be had from banks merging with non banking financial service firms. Berger and Humphrey (1994) concluded in a survey of US studies on recent scale economy that the average cost curve has a relatively flat U-shape with only small banks having the potential for scale efficiency gains and usually the measured economies are relatively small.

IGI GLOBAL PROOF

3. LITERATURE REVIEW There exists a great amount of literature on bank merger and acquisition in developed countries. But there has been little research effort on this issue in India, particularly using the approach – DEA. There are two types of studies relating to bank merger: Ex–ante studies, also called the event studies analysis considers the impact of mergers on market value of equity of both bidder and target banks on announcement of merger and Ex-post studies that on the other hand assesses the effect of merger on banks’ performance and efficiency by comparing pre and post merger performance of banks. Berger et al. (1999) provides an outstanding literature



Table 1. List of merger during post reform period in India Year of Merging

Target Banks

Acquirer Banks

Motive – Type of Merger

1993

New Bank India

Punjab National Bank

Restructuring of weak banks -Forced merger

1994

Bank of Karad Ltd

Bank of India


1995

Kashinath Seth Bank

State Bank of India


1996

Punjub Co-op Bank Ltd

Oriental Bank of Commerce


1997

Bari Doab Bank Ltd.



1999

Sikkim Bank Ltd

Union Bank of India


2000

Times Bank

HDFC Bank Ltd

Achieving of scale and scope economies Voluntary Merger

2001

Bank of Madura

ICICI Bank

Achieving scale and scope economies -Voluntary Merger

2002

ICICI Limited

ICICI Bank

Universal Banking - Voluntary Merger

2002

Benaras State Bank Ltd.

Bank of Baroda


2003

Nedungadi Bank Ltd.

Punjab National Bank


2004

South Gujarat Local Area Bank

Bank of Baroda


2004

Global Trust Bank



2005

Bank of Punjab

Centurion Bank

Achieving of scale and scope economies Voluntary Merger

2006

Ganesh Bank of Kurandwad

Federal Bank


2006

United western Bank

IDBI Bank


2006

Lord Krishna Bank

Centurion Bank

Expansion of Size – Voluntary Merger

2007

Single Bank

ICICI Bank

Expansion of Size – Voluntary Merger

2007

Bharat overseas Bank

Indian Overseas Bank

Regulatory Intervention

2008

Centurion Bank of Punjab

HDFC Bank

Expansion of Size, benefit of scope economy– Voluntary Merger

2008

State Bank of Saurashtra

State Bank of India

Expansion of Size, benefit of scale and scope economy– Voluntary Merger

2010

THE BANK OF RAJASTHAN LTD.

ICICI BANK LTD

Expansion of Size -Voluntary Merger

2010

State Bank of Indore

State Bank of India

Expansion of Size, benefit of scale and scope economy– Voluntary Merger

IGI GLOBAL PROOF

Source: Compiled from Report on Trend and Progress of Banking in India, RBI, various issues

Studies on scope economies found no evidence of these economies In Indian context, Jayadev et al. (2007) revealed that in the case of forced mergers, neither the bidder nor the target banks’ shareholders had benefited. But in the case of voluntary mergers, the bidder banks’ shareholders gained more than those of the target banks. Another study by Jayadev et al. (2010) found that many

Indian banks exhibit potential cost savings from mergers provided they rationalise their branch networks although profit efficiency may not rise immediately. Gourlay et al. (2006) analyzed the efficiency gains from mergers among Indian banks over the period 1991-92 to 2004-05 and observed that the merger led to improvement of efficiency for the merging banks. Reserve Bank of India (2008) agreed with this observation and



found that public sector banks have been able to get higher level of efficiency than private sector banks during post merger period. But, Ravichandran et al. (2010) suggested that the mergers did not seem to enhance the productive efficiency of the banks. Kaur et al. (2010) examined empirically the cost efficiency of bank mergers during post reform period and found, to some extent, merger programme has been successful in Indian banking sector. The Government and Policy makers should not promote merger between strong and distressed banks as a way to promote the interest of the depositors of distressed banks, as it will have adverse effect upon the asset quality of the stronger banks. In sum, the international and Indian experience does not provide strong evidence on merger benefits in the banking industry. However, there are no studies existing in the literature in the same direction as the present study adopts. Therefore, the present study will be able to throw further light on the existing banking merger literature by exploring the efficiency status of proposed merger of SBI and its subsidiaries based on the DEA evaluation and anticipate whether this merger scheme will produce a fully efficient bank in the Indian banking industry.

4.1. Models Used Several different mathematical programming DEA models have been proposed in the literature. Essentially, these models seek to establish which of n DMUs determine the envelopment surface or best practice frontier or efficient frontier. The geometry of this surface is prescribed by the specific DEA model employed (Kumar & Gulati, 2009). There are two types of efficiencies- input oriented and output oriented. Input oriented efficiency aims at reducing input amounts as much as possible while keeping at least the present output levels and output oriented technical efficiency maximizes the output level while using at least the present input levels. In the present study, we have utilized input oriented two basic DEA models - CCR and BCC.

4.1.1 CCR Model (Named After its Developers Charnes, Cooper and Rhodes, 1978)

IGI GLOBAL PROOF

4. METHODOLOGY AND DATA SOURCES As already pointed out, the study has followed the technique- data envelopment analysis (DEA) to estimate the technical efficiency of Indian banks. DEA is a non-parametric performance assessment methodology originally designed by Charnes, Cooper and Rhodes (1978) to measure the relative efficiencies of organizational units or decision making units (DMUs) under evaluation from the identical input output data set. The DEA approach applies linear programming techniques to constructs an efficient production frontier based on best practices over the data set. Each DMU’s efficiency is then measured relative to this frontier.

Assuming that there are n DMUs to be evaluated [DMUj (j = 1, 2… n)]. Each DMU consumes ‘m’ different inputs of identical nature for all DMUs [xij (i = 1,2, …..,m)] to produces ‘s’ different outputs of identical nature for all DMUs [ yrj (r = 1,2, …….,s)]. s  m  Minθk − ε ∑ si − + ∑ sr +    i− r + θk , λ, s , s i =1 r =1

(

)

Subject to n

∑x

ij

λj + si − = θk x ik

i = 1, 2..., m

∑y

rj

λj − s r + = yrk

r = 1, 2,...., s

j =1 n j =1

λj ≥ 0 i−

s ,s

r+

j = 1, 2,..., n ≥0

for all i and r

Where,



xij = Amount of input of i utilized by the jth DMU yrj = Amount of output of r produced by the jth DMU xik = Amount of input of i utilized by DMUk yrk = Amount of output of r produced by DMUk θk= efficiency score of DMU ‘k’ being evaluated λs represent the dual variables which identify benchmarks for inefficient units. Slack variables - si- (input slacks), sr+ (output slacks) Here ε > 0 is non-Archimedean element defined to be smaller than any real number and to be accommodated without having to specify the value of ε. The above model is an input-oriented model and assumes constant returns to scale of operation to measure overall technical efficiency (OTE). Optimal value θk reflects the OTE score of DMU ‘K’. It needs to solve ‘n’ times to get efficiency score for each DMU under evaluation. If θk = 1 and si- = sr+ = 0, then DMUK is CCR efficient otherwise CCR inefficient.

However, in the context of banking efficiency measurement, there are mainly two approaches to deal with this problem: Production Approach and Intermediation Approach. The main difference between these two approaches is the use of deposit as input or output. Berger and Humphrey (1997) pointed out that neither of these two approaches is perfect because they cannot fully capture the dual roles of banks as intermediaries of financial services as well as service providers. Therefore none is universally accepted approach. Berger and Humphrey (1997) suggested that intermediation approach is best suited for analyzing bank level efficiency where Production Approach for branch level efficiency. Therefore, given this and as a majority of the empirical literature, present study adopts Intermediation Approach for selecting input and output variables for estimating bank level efficiency. Literature on inputs and outputs specification for measuring bank efficiency in India summarizes that most of the studies rely on three inputs: a) Fixed assets as a proxy of physical capital b) No. of Employees as labour c) Deposit as material and two outputs: a) Interest Income b) Non-interest Income. In place of interest income some studies choose advance and investment in the output vectors. With this existing literature, the study selects three input and three output variables (Table 2). The choice of input and output variables is mainly guided by operational pattern, objectives of the Indian banking system in the post reform period and the availability of data (Baidya, 2012). ‘Fixed assets’ are included in input variables as a proxy of physical capital. The variable ‘No. of Employees’ is selected in this study as a proxy of labour like previous researches. ‘Loanable Fund’ includes deposit plus borrowing. ‘Deposit’ includes all types of deposit: demand, savings and time. Most of the studies in India use it as an input. ‘Net Interest Income’ is also called spread computed by subtracting interest expenses from interest income. This variable represents the performance of the traditional activities of banking. The output variable ‘Non-interest Income’ accounts

IGI GLOBAL PROOF

4.1.2 BCC Model (Named after Bankers, Charnes and Cooper, 1984)

BCC model differs slightly yet remarkable from CCR model with an additional constraint n

∑λ j =1

j

= 1

in the CCR envelopment model. This constraint is called convexity constraint in mathematics literature. It imposes of assessing the efficiency under variable returns-to-scale.

4.2. Identification of Strategic Variables The most challenging task to the researchers for estimating efficiency of banks through DEA methodology is to select appropriate and relevant inputs and outputs. The choice of inputs and outputs largely affects the derived efficiency level. There is no consensus on what constitutes inputs and outputs of banks.



Table 2. Input/ output specs for banking experience Input Variables

Output Variables

Fixed Assets Number of Employees Loanable Fund

Net Interest Income Non-interest Income Net Profit

for income from off-balance sheet activities such as commission, brokerage and so on. The inclusion of this variable enables the capturing of recent changes in banking services. Most of the studies in India use it as an output. The input output variables set (Table 2) is almost same as in the study of Kumar and Gulati (2008b) with only exception of accommodating an output variable – net profit. This variable helps to capture the profitability aspect of Indian banks. This also incorporates indirectly non labour operating expenses which the banks are incurring heavily for technology up gradation. Input and output variables of hypothetically merged SBI are measured by simply aggregating the present input and output values of SBI and its six subsidiaries existing in the year 2009-10. All the variables except ‘number of employees’ are measured in terms of rupees in lakhs.

individual banks, collected mainly from the ‘Statistical Tables relating to Banks in India’ and ‘Report on Trend and Progress of banks in India’ for the financial year 2009-10, available on the official website of Reserve Bank of India (http://rbi.org.in).

5. ANALYSIS AND FINDINGS We will hypothetically merge SBI and its six subsidiaries to form a new bank called merged SBI (MSB) having the sum of actual inputs and outputs of these seven banks as its inputs and outputs. We, therefore, measure efficiencies of selected banks separately under two situations: before merger (31 banks) and after merger (25 banks) by adding this new bank in place of seven banks of SBI group to the data set. Present study is mainly concentrated on the efficiency evaluation of hypothetically merged SBI. DEA scores are derived by using DEA software ‘DEA-Solver Learning Version 3’ designed by Cooper et al. (2007). Present study has selected three inputs (m = 3) and three outputs (s = 3) with a sample size of 31/25 (n = 25/31). Therefore, the sample size in this study exceeds the desirable size as per the rule of thumb (31/25>18) i.e., n (number of DMUs) equal to or greater than [max {m × s, 3 x (m + s)}] (Cooper et al., 2007). Thus, selected number of input and output variables allows accepted number of degree of freedom i.e., efficiency discriminatory powers. It is also found that there is a high correlation between selected input and output variables. So, with this appropriate number of inputs and outputs, sample banks selected taking into account of more homogeneity condition and reasonable validation by high degree of correlation between

IGI GLOBAL PROOF

4.3. Sample and Data Source 4.3.1 Sample Banks Yeh (1996) states that it is important to take into account the homogeneity condition during the choice of DMUs to make the DEA result more realistic. Giving more emphasis on the criteria of homogeneity condition, the present study selects 31 commercial banks (including 7 banks of SBI group) in India operating in 200910 having number of branches more than 650. Selected 31 sample banks with their codes used have been provided in the Appendix.

4.3.2 Data Source All the data are annual and secondary in nature. Annual bank level data are obtained from the published annual accounts (Balance Sheet and P&L Account) in Annual Reports of the



Table 3. The summary statistics of three types DEA efficiencies

No. of DMUs Evaluated

OTE

PTE

SE

31

31

31

Average Score

0.810

0.852

0.948

SD

0.174

0.160

0.071

Maximum

1

1

1

Minimum

0.427

0.474

0.711

No of Efficient Banks

9

12

9

Note: OTE – Overall Technical Efficiency measured by CCR model, PTE- Pure Technical Efficiency measured by BCC model and SE- Scale Efficiency measured by OTE/ PTE

input and output variables, the present study can demand more robust and reliable results.

5.1. Examination of Relative Efficiencies Before Merger Situation From the descriptive statistics of OTE measured using CCR model under constant returns-toscale presented in Table 3 calculated from scores given in the Appendix, only 9 banks out of 31 under evaluation (namely ANB, COB, INB, SBR, SBT, FDB, AXB, HFB, and ICB) are found to be 100% technical and scale efficient i.e., CCR score or OTE = 1 with a mean score 81%. Thus, Indian banks could have saved on an average 19% of the present input consumption in order to produce the present level of output. BCC scores under variable returns-to-scale show that IDB, PNB and SBI are accorded efficient status in addition to the 9 CCR efficient banks. The mean results also indicate that main source of overall technical inefficiency (OTIE) of Indian banks is pure technical inefficiency i.e., managerial underperformance. All banks belonging to SBI group are found to be above the average of entire sample in all types of DEA efficiency (Table 4) except of SBM in CCR and Scale sore. SBM is found to be the worst bank in terms of three efficiency parameters followed by SBP, SBH, SBJ, and SBI. SBI is very close to full CCR efficiency.

COB, AXB, FDB, and SBR are frequently appeared in the reference banks. Returns to scale under the heading ‘RTS’ as indentified by the input oriented BCC model indicates that SBR and SBT with full efficiency under both CCR and BCC score are operating in most productive scale size, the region where constant returns-toscale prevails. Among other five banks, three banks (SBI, SBJ, SBH) are operating at DRS and two banks (SBM, SBP) at CRS. From the scale scores, it is observed that all the banks of this group are very close to 100% scale efficiency except SBM i.e., operating at correct scale. Table 5 indicates that all the CCR inefficient banks of this group have positive weights in all the inputs except SBI and SBJ for employees and SBM for fixed assets as because of presence of non-zero slacks with these inputs. So reduction in inputs having positive weights will contribute directly to improve their CCR efficiency level. Whereas inputs with non-zero slacks need to reduce slacks amount before these inputs come to have positive impact on their efficiency estimation. Table 5 also depicts that there is a positive weights of all the banks in respect of outputs ‘Net interest Income’ and ‘Non-interest income’ except SBM for ‘Noninterest income’ while all the banks have nonzero slacks associated with output ‘net profit.’ All slacks are zero for SBR and SBT as because of they are fully CCR efficient. These are the background of efficiency status of the seven banks which are proposed to be merged.

IGI GLOBAL PROOF



Table 4. Efficiencies and returns to scale of seven target banks CCR Score

BCC Reference

Score

Scale Score

RTS

SBI

0.994

COB,SBR,AXB

1

DRS

0.994

SBJ

0.949

SBR,FDB,AXB

0.956

DRS

0.993

COB,SBR,SBT,FDB

0.941

DRS

0.993

1

CRS

1

SBH

0.934

SBR

1

SBM

0.709

AXB,HFB

0.899

CRS

0.789

SBP

0.912

COB,SBR,SBT,FDB

0.912

CRS

1

SBT

1

1

CRS

1

5.2. Examination of Relative Efficiency After Merger Situation

SBR, SBT in before merger efficiency evaluation. However, merged SBI (MSB) has a CCR efficiency, 0.947, which is less than one i.e., it is an inefficient bank. Here, it is to be pointed out that MSB is found to be a marginally inefficient bank (overall position 8th out of 25 banks under evaluation) having the highest efficiency score among inefficient banks. So, in order to arrive at more efficiency status, merging and restructuring of

From the descriptive statistics of OTE presented in Table 6, only 7 of 25 banks (ANB, COB, INB, FDB, AXB, HFB, and ICB) under evaluation are found to be 100% technical and scale efficient i.e., OTE = 1 with average OTE score of the entire sample is 78.2%. These seven banks are also found to be fully CCR efficient along with

IGI GLOBAL PROOF

Table 5. Optimal weights and slacks of seven target banks as per CCR model Weights

Slacks

Inputs

SBI

Outputs

Inputs

Outputs

Loanable Fund

Net Interest Income

NonInterest Income

Net Profit

No of employees

Fixed Assets

Loanable Fund

Net Interest Income

NonInterest Income

Net Profit

1.2E-06

5.3E09

2.78E07

2.2E-07

0

64468

0

0

0

0

221466

6.19E06

3.4E-06

0

2732

0

0

0

0

1297

No of employees

Fixed Assets

0

SBJ

0

2.1E-05

1.2E07

SBH

7.4E06

1.5E-05

5.7E08

5.00E06

3E-08

0

0

0

0

0

0

1801

SBR

1.8E05

3.7E-05

1.4E07

1.23E05

7.5E-08

0

0

0

0

0

0

0

SBM

5.7E05

0

1E-07

5.74E06

0

0

0

20926

0

0

22148

2770

SBP

8.8E06

1.8E-05

6.7E08

5.93E06

3.6E-08

0

0

0

0

0

0

19340

3.3E-05

6.2E08

7.14E06

0

0

0

0

0

0

0

0

SBT

0



Table 6. The summary statistics and MSB’s DEA efficiencies OTE

PTE

SE

25

25

25

Average score

0.782

0.837

0.934

SD

0.176

0.172

.085

No. of DMUs evaluated

Maximum

1

1

1

Minimum

0.426

0.473

0.673

No of Efficient Banks

7

10

7

MSB(Merged SBI)

0.947

1

0.947

SBI group needs reduction in input resources by on an average 5.3% of present level of inputs used to produce present level of outputs. PTE (BCC scores) indicates 3 more banks (MSB, PNB, and IDB) in addition to 7 CCR fully efficient banks are 100% technical efficient but not 100% scale efficient with a mean score 83.7%. In the case of after merger situation, Indian banks are also experiencing higher scale efficiency (93.4%) than PTE (83.7%).

on average, PTIE as the main source of OTIE. It is also find out scale inefficiency of MSB arises because of operating at the region where decreasing return to scale (DRS) prevails. So, MSB may face the problem of scale inefficiency which is due to DRS of operation.

5.2.2. Efficiency Improvement Plan for Merged SBI

IGI GLOBAL PROOF

5.2.1. Decomposition of Technical Efficiency

There is a relation among these three types of DEA efficiencies – OTE, PTE, and SE. This relationship is popularly known as decomposition of efficiency in DEA literature. Following relationship demonstrates a decomposition of efficiency. OTE = PTE × SE This decomposition depicts that main source of overall technical inefficiency (OTIE) of the Indian banks as a whole is caused by pure technical inefficiency (PTIE) i.e., inefficient operation of the bank itself as indicated in summary statistics parameters (Table 6). But main source of overall technical inefficiency of MSB is scale inefficiency i.e., inefficiency is mainly caused by the disadvantageous conditions under which the MSB would have to operate. It is to be pointed out that efficiency scores (before merger) of seven banks of SBI group indicate

DEA methodology provides how an inefficient DMU (bank) becomes fully efficient by indicating the level of inputs to be utilized and level of outputs to be produced. This is called projection in DEA literature which shows input and output improvement or input and output target for inefficient banks. Table 7 shows the CCR projection i.e., input output improvement plan for merged SBI, identified as a CCR inefficient bank by reducing their present level of inputs and enhancing present level of outputs. MSB can be 100% CCR efficient through the three stages of input output improvement plan. OTE of this bank is 94.75% and OTIE = 5.25% Input projection is presented with a negative sign and output projection with positive sign as because of inputs to be reduced and output to be enhanced for efficiency improvement philosophy. OTE - Overall Technical Efficiency, OTIE – Overall Technical Inefficiency, Input reduction in excess of OTIE indicates presence of non-zero input slacks and output augmentation indicates presence of non-zero



Table 7. Input & output improvement plan for merged SBI Present level

Inputs

Outputs

Improvement Radial

Slack

Total

Target level

Fixed Assets

621320

- 32606.13 (-5.25%)

0.00

- 32606.13 (-5.25%)

588713.86

No of employees

267332

-14029.26 (- 5.25%)

- 84009.96 (-31.42%)

- 98039.22 (-36.67%)

169292.77

Loanable Fund

122965291

- 6453073.08 (-5.25%)

0.00

- 6453073.08 (-5.25%)

116512217.92

Net Interest Income

3172517

0.00

0.00

0.00

3172517.00

Non-Interest Income

1839366

0.00

0.00

0.00

1839366.00

Net Profit

1243262

0.00

255901.49 (20.58%)

255901.49 (20.58%)

1499163.50

Note – Input Improvement = present Input values x (1 – efficiency score obtained) + Input Excess/ Input Slack if any. Output Improvement = Output shortfall/output (slack if any. Input target = Present level – Input Improvement Output Target = Present level + output Improvement

output slacks since we follow input oriented model.

current level of ‘Net Profit’ has no effect while other two outputs ‘Net interest income’ and ‘Non- interest Income’ play a positive role on the efficiency estimation procedure.

IGI GLOBAL PROOF

Stage I - It has to reduce its all inputs level by 5.25%. This proportional reduction is also known as radial adjustment in DEA. By this adjustment MSB becomes weakly efficient (fulfill the ‘Farrell’ or ‘week’ efficiency) (Cooper et al., 2007). Stage II – It has to reduce in addition to 5.25%, Input-II (No. of Employees) by 31.42% (36.67% - 5.25%). No further reduction is required for Input I and III since slack values corresponding to these inputs are nil. Thus, these two inputs- ‘Fixed Assets’ and ‘Loanable Fund’ have the positive impact; whereas present level of input- ‘No of Employees’ has no any effect on efficiency evaluation of MSB. Stage III - It has to enhance only output III (net profit) by 20.58% as because of presence of non-zero slack corresponding to this output. No adjustment is required for other two outputs. Since we follow input oriented model, it does not need any radial adjustment for outputs by the OTIE. Thus,

Stage II & III are known as slack adjustments in DEA. However, these slack adjustments in Stage II & III after radial adjustment in Sage I makes MSB strongly efficient (fulfill the ‘Pareto- Koopmans’ or ‘strong’ efficiency) (Cooper et al., 2007). Thus, efficiency improvement plan results in a really drastic reduction of input ‘no. of employees’ by about 98000 and enhancing output ‘net profit’ by about Rs 25590 lakhs in order to bring in merged SBI onto the efficient frontier.

5.2.3. Peer Banks for Merged SBI One of the important advantages of DEA methodology is to identify peer banks for each inefficient bank based on the positive lambda values of the efficient banks for an inefficient bank under consideration. The banks which provide the best practice of input utilization



form a reference set or peer banks of the inefficient banks. Based on CCR model, Peer banks of MSB are COB, AXB, and FDB since their lambda values are positive corresponding to the efficiency score of the bank MSB. To improve efficiency, MSB bank should follow their good operating practices since their input output configurations are similar with MSB, an inefficient bank. Lambda values of COB, AXB, and FDB approximately 6.24, 5.40 and 2.05 respectively show the proportion contributed to the point used to evaluate MSB. That is projected inputs value of MSB =(Input value of COB) × 6.24 + (Input value of AXB) × 5.40 + (Input value of FDB × 2.05) Similarly projected output value of MSB =(Output value of COB) × 6.24 + (Output value of AXB) × 5.40 + (Output value of FDB × 2.05)

OTE = 1 with a mean score 81%. Thus, Indian banks could have saved on an average 19% of the present input consumption in order to produce the present level of output. Out of 9 fully CCR efficient banks, two banks are from SBI group namely State bank of Indore and State Bank of Travancore. State Bank of India attains full BCC efficiency. All inefficient banks belonging to SBI group are above the average of entire sample in all types of DEA efficiency (Table 3) except of SBM in CCR and Scale sore. Within this group, SBM is found to be the worst bank in terms of three efficiency parameters followed by SBP, SBH, SBJ, and SBI. SBI is very close to full CCR efficiency. On the whole, managerial inefficiency of this group is the main source of overall inefficiency. It is observed that reduction in input resources of fixed assets and loanable fund will contribute directly to improve their overall technical efficiency. The results (after merger), reveal that merged SBI (MSB) has a CCR efficiency, 0.947, which is less than one (i.e., an inefficient bank). In order to be fully CCR efficient MSB has to reduce its present number of employees by 36.67%, fixed assets and loanable fund by 5.3% to produce present level of outputs. MSB is found to be fully BCC efficient. Thus, overall technical inefficiency of MSB is mainly due to scale inefficiency i.e., inefficiency is mainly caused by the disadvantageous conditions under which the MSB would have to operate. Scale inefficiency arises due to operating at the region where decreasing returns-to-scale prevails. Efficiency improvement plan results in a really drastic reduction of input ‘no. of employees’ by about 98000 and enhancing output ‘net profit’ by about Rs 25590 lakhs in order to bring in merged SBI onto the efficient frontier. The study first recommends that merging and restructuring of SBI group may not be enough. Reduction in input resources is also necessary to arrive at more efficiency status. Otherwise merger will result in a worsened efficiency position. For efficiency improvement, merged SBI should follow input output improvement plan given in Table 7 and operating practices of three peer banks – Corporation

IGI GLOBAL PROOF

Magnitude of lambda values indicates that MSB has more similarity to COB and then AXB, FDB.

6. CONCLUSION AND RECOMMENDATIONS It should be mentioned at this point that DEA measures relative efficiencies. Thus, all the results, comparisons of this study are based on the estimated relative efficiency which may differ from other studies. The objective of this paper is to measure and analyse the technical efficiency status of the proposed merger plan of SBI and its subsidiaries. For this, the study has adopted two DEA models - CCR and BCC to estimate efficiency of selected banks under two situations before and after hypothetical merger of seven SBI group banks for the financial year 2009-10. The results (before merger) reveal that only 9 banks out of 31 under evaluation are found to be 100% technical and scale efficient i.e., CCR score or



Bank, Axis Bank and Federal Bank. The study also recommends that merged SBI should follow input output configuration of its own subsidiary bank, State bank of Indore. It should explore marketing activities heavily to new areas to absorb its excess man power which ultimately would bring in higher profitability. We sincerely hope that this study opens a broad horizon for further researches to evaluate the relative efficiency of banks going for merger, using frontier approach and in turn will contribute for the development of Indian banks. The future research could extend our works by considering data for a longer period and other DEA models.

REFERENCES Akhavein, J. D., Berger, A. N., & Humphrey, D. B. (1997). The effects of bank mergers on efficiency and prices: evidence from the profit function. Review of Industrial Organization, 12, 95–139. doi:10.1023/A:1007760924829

Berger, A. N., & Humphrey, D. (1994). Bank scale economics, mergers, concentration, and efficiency: The U. S. experience (Center for Financial Institutions Working Paper No. 94-25). Philadelphia, PA: Wharton School Center for Financial Institutions, University of Pennsylvania Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. doi:10.1016/0377-2217(78)90138-8 Chong, B.-S., Liu, M.-H., & Tan, K.-H. (2006). The wealth effect of forced bank mergers and cronyism. Journal of Banking & Finance, 30, 3215–3233. doi:10.1016/j.jbankfin.2005.12.004 Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEASolver software (2nd ed.). New York, NY: Springer Science + Business Media. Cornett, M. M., & Teharnian, H. (1992). Changes in corporate performance associated with bank acquisitions. Journal of Financial Economics, 31, 211–234. doi:10.1016/0304-405X(92)90004-H Das, A. (1999). Profitability of the public sector banks: A decomposition model. Reserve Bank of India Occasional Papers, 20(1), 55-81.

IGI GLOBAL PROOF

Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264. doi:10.1287/mnsc.39.10.1261

Baidya, M. K. (2012). Efficiency study of large-sized banks in India – a DEA application. International Journal on Research in Commerce. Economics & Management, 2(3), 122–129. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Journal of Management Science, 30(9), 1078–1092. doi:10.1287/mnsc.30.9.1078

Das, A. (2000). Efficiency of public sector banks: An application of data envelopment analysis model. Prajnan Journal of Social and Management Sciences, 28(1), 119–131. Davis, S. I. (2000). Bank mergers lessons for the future. London, UK: Macmillan. doi:10.1057/9780230509399 Debasish, S. S. (2006). Efficiency performance in Indian banking: Use of data envelopment analysis. Global Business Review, 7(2), 325. doi:10.1177/097215090600700209

Bansal, K., & Bansal, M. (2011). Banking sector needs consolidation. Retrieved March 12, 2012, from http://www.deccanherald.com/content/39299/ banking-sector-needs-consolidation.html

Gorlay, R., & Weyman-Jones, T. (2005). Nonparametric analysis of efficiency gains from bank mergers in India (Working Paper No. 2006-17). Leicestershire, UK: Loughborough University.

Berger, A. N., Demetz, R. S., & Strahan, P. E. (1999). The consolidation of the financial services industry: Causes, consequences and implications for the future. Journal of Banking & Finance, 23, 135–194. doi:10.1016/S0378-4266(98)00125-3

Hannan, T., & Wolken, J. (1989). Returns to bidders and targets in acquisition process: Evidence from the banking industry. Journal of Financial Services Research, 8, 145–156.



Hawawani, G., & Swary, I. (1990). Merger, and acquisitions in the US banking industry, evidence from the capital markets. Amsterdam, The Netherlands: North-Holland. Jayadev, M., & Sensarma, R. (2007). Mergers in Indian banking: An analysis. Mumbai, India: Indian Institute of Banking and Finance. Retrieved July 25, 2011, from https://uhra.herts.ac.uk/dspace/ bitstream/2299/3465/1/902962.pdf Jayadev, M., & Sensarma, R. (2010). Efficiency, scale economies and valuation effects: Evidence from bank mergers in India. International Journal of Financial Services Management, 4(4), 311–337. doi:10.1504/IJFSM.2010.035683 Kaur, P., & Kaur, G. (2010). Impact of mergers on the cost efficiency of Indian commercial banks. Eurasian Journal of Business and Economics, 3(5), 27–50. Kumar, S., & Gulati, R. (2008a). Did efficiency of Indian public sector banks converge with banking reforms? International Review of Economics, 56(1), 47–84. doi:10.1007/s12232-008-0057-2 Kumar, S., & Gulati, R. (2008b). An examination of technical, pure technical, and scale efficiencies in Indian Public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33–69.

Nandy, D. (2007). How efficient the Indian banks are: A DEA approach. The Management Accountant, 42(10), 803–812. Narasimham, M. (1991). Report of the committee on the financial system. Mumbai, India: Government of India. Narasimham, M. (1998). Report of the committee on banking sector reforms. Mumbai, India: Government of India. Neely, W. (1987). Banking acquisitions: Acquirer and target shareholder returns. Financial Management, 16(4), 66–73. doi:10.2307/3666110 Pilloff, S. J. (1996). Performance changes and shareholder wealth creation associated with mergers of publicly traded banking institutions. Journal of Money, Credit and Banking, 28(2), 294–310. doi:10.2307/2077976 Pilloff, S. J., & Santomero, A. M. (1997). The value effect of bank mergers and acquisitions (Working Paper No. 97 (7)). Philadelphia, PA: The Wharton Financial Institutions Centre. Ravichandran, K., Mat-Nor, F., & Mohd-Said, R. (2010). Market based mergers in Indian banking institutions. International Research Journal of Finance and Economics, 37, 30–39.

IGI GLOBAL PROOF

Landerman, E. S. (2000). The potential diversification and failure reduction benefits of bank expansion into non-banking activities (Working Paper in Applied Economic Theory No. 2000-01). San Francisco, CA: Federal Reserve Bank of San Francisco. Miller, S. M., & Noulas, A. G. (1996). The technical efficiency of large bank production. Journal of Banking & Finance, 20(3), 495–509. doi:10.1016/03784266(95)00017-8

Mohan, R. (2006). Reforms, productivity and efficiency in banking: The Indian experience. Reserve Bank of India Bulletin. Retrieved May 12, 2010, from http://rbidocs.rbi.org.in/rdocs/Speeches/ PDFs/69198.pdf Mohan Ram, T. T. (2003). Long-run performance of public and private sector bank stocks. Economic and Political Weekly, 38(8), 785–788. Mohan Ram, T. T., & Ray, S. C. (2003). Productivity and efficiency at public and private sector banks in India. Ahmedabad, India: Indian Institute of Management. Retrieved May 19, 2008, from http://ideas. repec.org/p/iim/iimawp/wp01497.html

Ray, S. (2007). Are some Indian banks too large? An examination of size efficiency in Indian banking. Journal of Productivity Analysis, 27(1), 41–56. doi:10.1007/s11123-006-0022-6 Reddy, A. (2004). Banking sector liberalization and efficiency of Indian banks. IUP Journal of Bank Management, 3(2), 37–53. Reserve Bank of India. (2008). Efficiency productivity and soundness of banking sector. Report on Currency Finance, 2(2), 393–446. Reserve Bank of India. (2010). Report on trend and progress of banking in India, 2009–10. Mumbai, India: Author. Rezaee, Z. (2001). Financial institutions, valuations, mergers, and acquisitions (2nd ed.). New York, NY: John Wiley & Sons. Sathye, M. (2003). Efficiency of banks in a developing economy: The case of India. European Journal of Operational Research, 148(3), 662–671. doi:10.1016/S0377-2217(02)00471-X



SBI to take a view on merger of associates in mid-2012 (2011, August 6). Economic Times of India. Retrieved March 15, 2012, from http://articles.economictimes. indiatimes.com/2011-08-06/news/29858926_1_associate-banks-state-bank-bikaner-and-jaipur Spindt, P. A., & Tarhan, V. (1992). Are there synergies in bank mergers? New Orleans, LA: Tulane University.

Wheelock, D. C., & Wilson, P. W. (2004). Consolidation in US banking: Which banks engage in mergers? Review of Financial Economics, 13(1-2), 7–39. doi:10.1016/j.rfe.2003.09.001 Yeh, Q.-J. (1996). The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. The Journal of the Operational Research Society, 47(8), 980–988.

Vennet, R. (1996). The effect of mergers and acquisitions on the efficiency and profitability of EC credit institutions. Journal of Banking & Finance, 20(9), 1531–1558. doi:10.1016/S0378-4266(96)00014-3

IGI GLOBAL PROOF



APPENDIX Table 1A. Before Merger Scores

After Merger Scores

No.

DMUs

DMUs Code

CCR

BCC

SE

No.

DMUs

DMUs Code

CCR

BCC

SE

1

ALLAHABAD BANK

ALB

0.668

0.706

0.946

1

ALLAHABAD BANK

ALB

0.668

0.706

0.946

2

ANDHRA BANK

ANB

1

1

1

2

ANDHRA BANK

ANB

1

1

1

3

BANK OF BARODA

BOB

0.748

0.802

0.933

3

BANK OF BARODA

BOB

0.748

0.802

0.933

4

BANK OF INDIA

BOI

0.682

0.690

0.989

4

BANK OF INDIA

BOI

0.682

0.690

0.989

5

BANK OF MAHARASHTRA

BOM

0.509

0.601

0.847

5

BANK OF MAHARASHTRA

BOM

0.509

0.601

0.847

6

CANARA BANK

CAB

0.753

0.812

0.928

6

CANARA BANK

CAB

0.753

0.812

0.928

7

CENTRAL BANK OF INDIA

CBI

0.427

0.474

0.902

7

CENTRAL BANK OF INDIA

CBI

0.427

0.474

0.902

8

CORPORATION BANK

COP

1

1

1

8

CORPORATION BANK

COP

1

1

1

9

DENA BANK

DEB

0.716

0.807

0.887

9

DENA BANK

DEB

0.721

0.989

0.729

10

IDBI BANK Ltd.

IDB

0.884

1.000

0.884

10

IDBI BANK Ltd.

IDB

0.884

1.000

0.884

11

INDIAN BANK

INB

1

1

1

11

INDIAN BANK

INB

1

1

1

12

INDIAN OVERSEAS BANK

IOB

0.657

0.664

0.989

12

INDIAN OVERSEAS BANK

IOB

0.657

0.664

0.989

13

ORIENTAL BANK OF COMMERCE

OBC

0.819

0.887

0.923

13

ORIENTAL BANK OF COMMERCE

OBC

0.819

0.887

0.923

14

Punjab and Sind Bank

PSB

0.668

0.939

0.711

14

Punjab and Sind Bank

PSB

0.668

0.992

0.674

15

PUNJAB NATIONAL BANK

PNB

0.903

1.000

0.903

15

PUNJAB NATIONAL BANK

PNB

0.903

1.000

0.903

16

SYNDICATE BANK

SYB

0.698

0.722

0.967

16

SYNDICATE BANK

SYB

0.704

0.722

0.975

17

UCO BANK

UCB

0.608

0.609

0.998

17

UCO BANK

UCB

0.608

0.609

0.998

18

UNION BANK OF INDIA

UNI

0.706

0.707

1

18

UNION BANK OF INDIA

UNI

0.706

0.707

1.000

19

UNITED BANK OF INDIA

UBI

0.509

0.558

0.911

19

UNITED BANK OF INDIA

UBI

0.509

0.558

0.911

20

VIJAYA BANK

VJB

0.645

0.712

0.905

20

VIJAYA BANK

VJB

0.645

0.731

0.882

21

STATE BANK OF INDIA

SBI

0.994

1

0.994

21

FEDERAL BANK

FDB

1

1

1

22

STATE BANK OF BIKANER and JAIPUR

SBJ

0.949

0.956

0.993

22

AXIS BANK

AXB

1

1

1

23

STATE BANK OF HYDERABAD

SBH

0.934

0.941

0.993

23

HDFC BANK

HFB

1

1

1

IGI GLOBAL PROOF

continued on the following page



Table 1A. Continued 24

STATE BANK OF INDORE

SBR

1.000

1.000

1.000

24

ICICI BANK

ICB

1

1

1

25

STATE BANK OF MYSORE

SBM

0.709

0.899

0.789

25

MERGED STATE BANK

MSB

0.948

1.000

0.948

26

STATE BANK OF PATILALA

SBP

0.912

0.912

1

26

27

STATE BANK OF TRAVANCORE

SBT

1

1

1

27

28

FEDERAL BANK

FDB

1

1

1

28

29

AXIS BANK

AXB

1

1

1

29

30

HDFC BANK

HFB

1

1

1

30

31

ICICI BANK

ICB

1

1

1

31

IGI GLOBAL PROOF



A Study on the Contribution of 12 Key-Factors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by Using a Neural Network Model E. Stathakis, Kavala Institute of Technology, Greece M. Hanias, University of Athens, Greece P. Antoniades, Kavala Institute of Technology, Greece

IGI GLOBAL PROOF L. Magafas, Kavala Institute of Technology, Greece

D. Bandekas, Kavala Institute of Technology, Greece

ABSTRACT This study gives a new methodological framework regarding the measuring of the contribution of some keyfactors on the regional growth rate and forecasting the future development rates, based on Neural Network Models (NN Models). It’s a serious attempt to study the contribution of twelve key-factors to the change of the Regional Gross Domestic Product of the Region of East Macedonia -Thrace during a long-term of growth process, by creating and using a suitable Neural Network Model. Specifically, twelve key-factors, time functioned in the period 1991-2008, are studied for the first time, in order to be investigated, scientifically, firstly their % contribution to growth of the regional economy and secondly, to be predicted how much the (Regional Growth Domestic Product) RGDP-under certain conditions-will be changed. It’s a NN Model with inputs the twelve key-factors in order to be evaluated and measured, at the best precise, their percentage contribution to the RGDP. The model and results can be found further into the article. Keywords:

East Macedonia, Future Forecasting, Neural Networks (NN), Regional Gross Domestic Product, Regional Growth Rate, Time Series

DOI: 10.4018/ijpmat.2012010102 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

InternationalJournalofProductivityManagementandAssessmentTechnologies,1(1),18-28,January-March2012 19

INTRODUCTION, BRIEF LITERATURE, AND SOME EXPLANATIONS Considering, in theoretical basis the regional problem, we can say that the two predominant economic growth approaches, we mean the neoclassical model and the model of endogenous growth, are in force even to the regional growth. Romer (1986) and Rebelo (1991), argue that the absence of convergence and the presence of disparities and inequalities across economies of various spatial levels throughout the world represent strong evidence against the neoclassical model and in favor of the theories of endogenous growth. But, some newer economists, fans of the neoclassical economic school, argued that the neoclassical growth model amended with partial capital mobility and reinforced by technological diffusion, explain better the issue of convergence/divergence in region level (Baro, 1991). But undoubtedly, the overexploitation of some resources in certain areas and the inactivation of some resources in other areas, in a status of widened open and transacting economy, lead toward to be formed developmental disparities and inequalities into and among areas or regions and, the result is what interprets well the growth gap between north-south. In Greece, the regional problem is enough significant and complicated, since the inequalities and disparities among the thirteen regions are greater than to other euro zone countries. An analysis of data derived from databanks of Hellenic Statistical Service (HSS) shows that up to end of ‘80s the polar pattern was predominant in Greece (Petrakos & Saratsis, 2000). Also, from the analysis of such data, we conclude that the dynamic developing sectors are concentrated in three areas, greater area of Athens, Thessalonica, and urban-industrial complexes along the growth axis between Athens-Thessalonica. Also, local development advantages were present in regions around the above complexes. Despite the state policies for a new regional strategy aiming to reduce the disparities among the thirteen regions, even

today, the different developmental levels continue to be existed, since five regions from the thirteen ones have, at least, a 20% lower GDP/ capita than the eight others in average terms, a growth divergence greater than anyone or a European society can accept. In order anyone can satisfactory explain when, why and how the convergent or divergent growth among regions of a country take place, he will have to collect, process and present multiannual statistical series -without significant time lags pertaining to regional socioeconomic conditions. Of course, it is much more significant to collect data which are more spatially specific than the statistical series already available at the national level. Take in account all the previous, we do remark the difficulties anyone meets to collect statistical data at regional level in Greece, since to this country enough trustworthy and uniform statistical data at regional level have been collected by the NSSG for the last fifteen years only. So, our attempt to collect analyze, and present such time series of eighteen years, 1991-2008, was very great deal. Even, the data used by us were adapted and formed in a uniform type in order to be able to be compared for the first time. Another key part of this work is to look behind the factors of success for explanation of EMTH region growth and spatial performance, using very specialized method from Econo(mo)Physics. Econo(mo)physics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve very complex problems in economics. Usually, such models have used in financial economic and more specifically are used in the prediction of future behaviour of shares in the largest Stock Exchange Markets. In this work, another particularity is that we have used a NN Model able to produce results using even small time-series- only twenty years, although this model is suitable for the case of many inputs. So, it was needed to do many adaptations in order the model to become functioning and reliable. A neural network is a computational model that is loosely based on the neuron cell structure

IGI GLOBAL PROOF



of the biological nervous system. Given a training set of data, the neural network can learn the data with a learning algorithm. In this research, the most common algorithm, back propagation, is used. Through back propagation, the neural network forms a mapping between inputs and desired outputs from the training set by altering weighted connections within the network. In order to construct a neural network that predicts the risk weighted GDP we need the correct inputs-data- which are the key-factors. Factors, like RGDP as total and per capita, energy consumption (at industry, housework, and service sector), R&D investment, etc, are the key-factors that have main influenced and been influenced the socioeconomic growth of the AMTH region, during the last three decades, since Greece became an EC member. The most common measure -but not describing clearly the growth effectiveness problem- about the growth level of a region is the real growth of R-GDP (total and per capita). Of course, there are some other special developmental factors, based on sectoral structure of the regional economy and employment that give a clearer image in quantative and qualitative basis of the growth. From the analysis of the interdependencies among the twelve factors a very useful result is produced. Many experts claim that the most explanatory approach about the regional growth issue is the investigation of relations between real growth of R-GDP and the other special factors which will be studied to this work. Our contribution to this direction is the present paper since, we try to study and investigate how and how many the twelve key-factors influence the RGDP of EMTH using Physical Models as Neural Networks. The evaluation results denote that NN Model is applicable to our data producing very interesting results about the importance of the inputs (key-factors), as well as, on the prediction of RGDP development, and, as additional result, what type of growth policies will must be followed by decision making units.

BRIEF PROFILE OF THE REGION The region of RMETH located in northeast of Greece and covers an area of 14.157 km2 and has a population of 612.000 (HSS-2008). The urban population is 42.7% of the total one, a percentage below the corresponding of the country -62.3%. Gender labor structure or participation of men and women in workers is 68.7% and 37.4% respectively. The region’s percentage of high and very well-skilled people is lower than the corresponding national level, 0.38% and 0.83% respectively. The ratio of R&D expenditure expressed as % of the regional GDP was 0.20-0.45%, it is enough lower compared with the corresponding national ratio of 0.40-0.92%. The number of the industrial units, employing 10 or more persons, as a percentage of the total number of manufacturing units, is ever lower of 5%, a little higher of the corresponding national figure of 4,72%. High technology industry is considered relatively limited in extend, but the same rates are existed to national average. The region is crossed by the main national and international highway networks (Egnatia, etc.) and, speaking generally, we can say the existed infrastructure is enough satisfactory able to support more dynamic, competitive and viable growth. The Greek policy-makers, into the frame of the new European Regional Development Programs (ESPA 2007-13), have focused on:

IGI GLOBAL PROOF

a. Serious and smart investments in primary sector (organic foods), in secondary sector (food and beverage processing, etc), and in alternative forms of tourism. b. Exploitation of some natural resources like geothermal fields for large scale applications, solar and wind energy applications, etc. c. Providing more smart and effective investment incentives which will attract serious investors to invest in globally competitive manufactures.



d. Promotion of new productive sector with high potential exploiting the geostratigical position of the region in SE Europe. e. Promotion of applied research and vocational education.

REASONING THE CHOICE OF THE TOPIC OF THE WORK The reason led us to deal with this topic is that, although the Region of EMTH has some natural, economic and socioeconomic advantages able to drive it to great growth rates, it didn’t happen and anyone can’t satisfactory explain its present, not good, economic situation. The area has very significant comparative and competitive advantages, it is full of natural resources (mines, geothermal fields, winds, agricultural products, etch.), and has the most important geographical and geostrategical position in the wide SE Europe. Also, its human power is skillful enough, able to support a globally competitive productive system. Besides, it continues to be ranked among the purest regions- at the 298 position among 314 ones of the EU. Many macroeconomic and socioeconomic indices prove that fact and it is very important for anyone to investigate in depth this case and to give a well-documented explanation. After that, we decide to give a specific explanation on this phenomenon using a modern tool, the method of Neural Network Analysis. The worth and utility of Neural Network Models lies in the fact that “they can be used as functions f(x) form inserting as variables observations, for instance time-series”. NNM’s are very useful in many applications where, the complexity or uniformity of the data or tasks makes the design of such functions f(x) by hand very impractical. In our case, the task to which neural network model is applied tends to fall within the category of functions approximation or regression analysis including time-series prediction and modeling. The certain method chosen by us for this work is an intelligence method analysis of a small time series consisted only by eighteen points. However, this is the weak point for our

analysis. In order to construct, train and test a reliable neural network, we need much more data in time-series form. In other words, the more data, the better results. So, due to our small data set, our analysis is somewhat imperfect or very preliminary. The data in time series form were collected very difficult because of, in our country, statistical data in the level of region don’t be available in a systematic way for a long time of period. In counter, very often, the time-series of data have different data, presented in a different way. For this reason, we made many adaptations so that the data used by us to this work to have the same form for the entire period of time. Besides, our all adaptive interventions rely on statistical principles and they take forms enable us to elaborate them with a scientifically trustee way.

METHODOLOGY FRAMEWORK Despites the existing in economic theory of the growth many models capable to measure the regional development, a fact is sure, many and various factors participate to growth process and their relations with the growth and among them are very complicated and, sometimes, chaotic. So that, when we try, absolutely scientifically, to explain the when, why and how of the regional growth rates and diversification taking place, we meet many difficulties. This is because the economic growth is a complex, non linear, socioeconomic and multifactor phenomenon when it studied for a long-term of period. The problems and difficulties related to local, regional or national growth dynamics are linked with the interdependencies and interactions among the many growth determinants, influence and been influenced, that make the calculation and estimation of such correlations a very complex affair. So, our work is indeed an innovative approach that combines two disciplines, Economy (Econometrics) and Physics, which it is called Econo(mo)Physics. In order to implement the model, we collected important and related to growth statistical data for a period of eighteen

IGI GLOBAL PROOF



years, forming a time-series of twelve economic and socioeconomic factors/determinants-influence or being influenced- the region’s growth (used to our model as independed variables). The proposed determinants of regional growth can be categorized-according to regional or spatial economics- as: external conditionsthey can’t be influenced by the region, cyclical reversion-they are influenced by cyclical phenomena, structural policies, cyclical volatility and institutions. The model can produce very analytical, explanatory and useful result so that policy-makers can be helped to take the right and effective decisions. For instance, the NM model used can give information about which are the primary driving forces behind the fast growth of a region. Also, it can inform everyone on how determinants like, energy consumption by manufactures, households, or service industry, the gross value added produced by manufacturing sector (GVA-m), gross fixed investments in construction and manufacturing sectors, R&D expenditures by public and private sector and the quality of labor force, etc influence the changes of RGDP, RGTP/ capita. We mean that, the current prices of the determinants studied are converted to constant prices (2002) by using sectoral deflators in the level of country obtained by N.S.S database. In any case, for any conversion or adaptation done to the current prices of the determinants were followed all the scientific processes.

set of inputs are the data X1,X2,X3,X4,X5,X6, X7,X8,X9,X10,X11,X12 and the output is the Y data for year 1991, i.e., the RISK WEIGHTED GDP, so this is the first epoch. We feed the neural network with eighteen epochs. The network architecture is shown in Figure 1. There is a full interconnection between each layer (Lachtermacher & Fuller, 1995; Velido, Lisboa, & Vanghan, 1999). We choose for Hidden layer activation function the hyperbolic tangent function and for activation function the logistic function (Rumelhart, Hinton, &Williams, 1986; Karnin, 1990; Hornik, Stinchcombe, & White, 1989). We used as error function the sum of square and train the neural network until the mean square error be 0.1 (Murata, Yoshizawa, & Amari, 1994). The result of training procedure is shown in Figure 2, where the excellent training performance is demonstrated. We represent with dots the actual data, while the solid line represents the learned data. Each input has a different effect on learning and network performance which reflects the weighted input values. For the learning performance of Figure 2 the calculating relative importance of each input is shown in Figure 3. It’s clear, according the inputs appeared to Figure 3; the following order of the top-five determinants influenced the growth rates of the region of EMTH.

NEURAL NETWORK CONSTRUCTION

•

IGI GLOBAL PROOF

The Neural Network Model used to this paper is based on a certain number of data/ information made up a time series of eighteen points and covers the period from 1991 to 2008 (Table 1). We construct a back propagation network (Widrow & Lehr, 1990; Tsibouris & Zeidenberg, 1996; Armano, Marchesi, & Murru, 2005), that consists of one input layer, one middle or hidden layer and one output layer. The input layer has twelve processing elements (PE) neurons; the middle layer has also twelve neurons. The first

•

• •

GROSS EXPENDITURES OR INVESTMENTS FOR R & D AS % of GDP DOMESTIC CONSUMPTION OF ELECTRICITY INDUSTRIAL PRODUCTION INDEX: 1990 = 100 T O TA L C O N S U M P T I O N O F ELECTRICITY

The determinants play the major role in network performance in order to predict the RISK WEIGHTED GDP effecting over to 75% to the total RGDP. So, policy-makers in order to achieve high economic growth rates in the region of EMTh, the applied policies should be based on the previous four inputs or determinants.


2323

2323

2393

2507

2602

2004

2005

2006

2007

2008

723

732

706

714

711

699

712

727

685

533

489

474

470

481

448

455

467

439

648

602

552

512

498

471

447

426

396

455

431

421

417

413

401

392

390

386

784

767

748

710

703

684

665

612

594

581

553

528

512

481

454

430

416

395

ELECTRICITY CONSUMPTION BY HOUSEHOLDS (GWh)

X4

INPUT

95.4

97.7

100.6

103.2

104.6

104.9

105.2

106.5

109.1

109

109.2

108.7

105.7

103.4

101.5

100.2

97.8

99.9

MANUFACTURERS’ PRODUCTION INDEX: 1990 = 100

X5

INPUT

94.5

96.3

97.4

99.8

102.5

103.1

103.5

104.5

108.8

110.6

111.1

109.2

107.3

106.1

105.8

105.2

101.1

100

MANUFACTURERS’ PRODUCTION INDEX: 1990 = 100 FOR 4 ENERGY INTENSIVE SECTORS

X6

INPUT

0.06

0.09

0.08

0.07

0.11

0.14

0.17

0.12

0.14

0.08

0.09

0.12

0.12

0.09

0.08

0.09

0.08

0.08

45.9

48.7

49.8

50.2

51.3

51.5

50.7

52.4

51.9

52.1

53.2

54.2

54.1

56.8

57.1

52.9

53.1

52.8

X9

X8 INDUSTRIAL CAPACITY EXPLOITATION AS %

X7 GROSS INVESTMENTS FOR R& D AS % OF RGDP

61.8

66.3

67.5

67.6

68.7

69.1

69.2

70.4

72.1

70.6

77.1

75.2

74.5

72.2

70.4

62.4

68.3

66.4

PROFITABLE ENTREPRISE AS % OF TOTAL ONES

INPUT

INPUT

INPUT

IGI GLOBAL PROOF


Sources: 1.H.S.S 2. ICAP 3. http://www.economics.gr (data are elaborated and adapted by us in order to be uniform)

2297

1811

1999

2194

1765

1998

2003

1594

1997

2002

1581

1996

2065

1490

1995

2157

1416

1994

2000

1357

1993

2001

1209

1311

1991

1992

ELECTRICITY CONSUMPTION BY SERVICE SECTOR (GWh)

X3

X2

ELECTRICITY CONSUMPTION BY INDUSTRIES (GWH)

X1

TOTAL ELECTRICITY CONSUMPTION (GWh)

INPUT

INPUT

ΥΕΑΡ

INPUT

3.5

3.5

3.5

4

4

4

4.8

4.8

5.4

5.4

6

6

5.4

4.8

4.8

4

4

4

PERCENTAGE OF SME’s BELONGING TO ENERGY INTENSIVE INDUSTRIES

X10

INPUT

12.1

12.6

12.7

12.9

12.8

12.9

13

13.9

15.6

15.1

15.9

16.2

15.3

13.1

12.8

11.6

12.4

11.8

ENERGY CONSUMPTION BY SME’s BELONGING TO ENERGY INTENSIVE INDUSTRIES AS % OF TOTAL

X11

INPUT

3.808

5.084

6.636

11.341

7.503

7.25

7.596

6.537

5.362

5.175

6.444

5.088

4.918

4.041

3.8

3474

3.344

3.782

THE BUILDING NEW HOMES INDEX

X12

INPUT

3004.214779

3033.404285

2974.157243

3010.57037

2917.564592

2836.864662

2701.406991

2781.353109

2655.401355

2468.882018

2179.448396

2087.659948

2015.478121

2043.357747

1932.102788

1906.635211

1949.956859

2020

RISK WEIGHTED GDP

Y

OUTPUT


Table 1. Input and output items for the purposed neural network


Figure 1. Network architecture with one input layer with 12 neurons one hidden with 12 neurons layer and one output layer with one neuron

EXPLANATIONS OF TIMESERIES PREDICTION ABILITY In order to predict the time-series of RISK WEIGHTED GDP we construct a back propagation network that consists of one input layer, one middle or hidden layer and one output layer, as before mentioned. The input layer has five processing elements (PE) neurons, while

the hidden layer has eight ones. The network architecture is shown in Figure 4. There is a full interconnection between each layer, mentioned before. Then, we forecast (next) values from a number of earlier (previous) values. In other words, a number of sequential values are used to forecast the next value in the same sequence. Essentially, time-series problem is a special form of regression problem where someone

IGI GLOBAL PROOF

Figure 2. Actual data (dot) and learned data (solid line)



Figure 3. Calculating relative importance of each input item

needs to forecast a continuous value (Kohzadi, Boyd, Kermanshahi, & Kaastra, 1996). We apply the iterative procedure (Refenes, Zapranis, & Francis, 1994) where, we forecast one step ahead and then, we use this forecast as input; we forecast one more step ahead, etc. (Hanias & Karras, 2009;Hanias, Curtis, & Thalassinos, 2007). Of course, the more steps ahead forecast the less reliable the forecasted are. Beginning with the first value of the time-series, the first set of inputs is data Y1,Y2,Y3,Y4,Y5 and our outputs is Y6. Similarly, the second set of inputs is Y2,Y3,Y4,Y5,Y6 (forecasted output) and the output is the Y7. The third set of inputs is Y3,Y4,Y5,Y6 (forecasted output), Y7 (forecasted output) and the output is the Y8. The forth set of inputs is Y4,Y5,Y6(forecasted output), Y7 (forecasted output), Y8 (forecasted output), and the output is the Y9. So, from Y1,Y2,Y3,Y4,Y5 inputs we predict the Y6,Y7,Y8,Y9 values. There is a full interconnection between each layer. The network was training so that the mean square error MSE to be less of 0.1 (O’Leary,1998; Wong, Bodnovich, & Selvi, 1997) The actual and predicted time-series for four time steps (years) ahead are shown in Figure 5 while Table 2 includes the actual and predicted values for four time steps (years) ahead. According to Figure 5 the predicted RISK WEIGHTED GDP for 2008 will present a slight decrease with a tendency to be stabilized in the future (2009-2012). Possibly

this behavior indicates that a financial crisis is coming up and is detecting initially qualitatively.

CONCLUSION In the current study prediction of the Regional Growth Domestic Product (RGDP) of East Macedonia Thrace on yearly basis was attempted in two ways using neural net Models. First we predict the RGDP as an output of a neural network with twelve key parameters time functioned as inputs. These inputs are twelve economic and social factors/determinants which can be categorized as: external conditions, cyclical reversion, structural policies, cyclical volatility and institutions. The contribution of twelve key developmental factors to the growth of the region of EMTH and the prediction of how much the Regional Growth Domestic Product (RGDP) of EMTH will increase to the next years 2009, 2010, 2011, and 2012 using, for the first time, Neural Net Model. We choose twelve factors that are more different than which have been used to such studies by others and, therefore, this study investigates the issue of regional growth in a different and more deep way. Developmental factors like, gross investments in R&D as % of the RGDP, electricity consumption by industries, households, business belonging to service industry, the rate of building new homes, etc, have used, for the first

IGI GLOBAL PROOF



Figure 4. Network architecture with one input layer with 5 neurons one hidden with 8 neurons layer and one output layer with one neuron

time, in order to be measured their contribution to RGDP growth using the Neural Model It’s shown from our analysis that four factors, the GROSS BUSINESS EXPENSES FOR R & D% of GDP, DOMESTIC CONSUMPTION OF MWh, INDUSTRIAL PRODUCTION INDEX and TOTAL CONSUMPTION ELECTRICAL POWER TO MWh play the major role in network performance in order to predict the RISK WEIGHTED GDP, especially the two first factors. Second we faced the prediction of RGDP as the iterative time series prediction problem. For this purpose we used

again a neural network and achieved a prediction for time steps (years) ahead. We conclude that the contribution of four developmental factors is greater than 75%, and this means the policy-makers and regional growth planners will have to take in account seriously that result. In other words, the regional growth strategy will have to focus or based on investments or policies on such kind of “smart and soft infrastructure” that boost the smart and globally competitive growth direction than to easy and well-known heavy infrastructure like, highways, factories belonging to low or medium

IGI GLOBAL PROOF

Figure 5. Actual values (squares) and predicted values (dots) of RISK WEIGHTED GDP



Table 2. Actual and predicted values for 4 time steps (years) ahead YEAR

ACTUAL VALUES of RISK WEIGHTED GDP

PREDICTED VALUES of RISK WEIGHTED GDP

1996

2015.47812

2102.70264

1997

2087.65995

2091.28622

1998

2179.4484

2179.28649

1999

2468.88202

2467.40636

2000

2655.40135

2652.52195

2001

2781.35311

2781.01231

2002

2701.40699

2701.92333

2003

2836.86466

2835.5228

2004

2917.56459

2914.71967

2005

3010.57037

3015.89143

2006

2974.15724

2987.71471

2007

3033.40428

3012.56701

2008

3004.21478

2953.05767

2009

N/A

2980.20746

2010

N/A

2989.45092

2011

N/A

2975.54596

2012

N/A

2991.57122

IGI GLOBAL PROOF

technology industries, etc. The developmental level of the REMTH needs now much more qualitative than quantitative growth approaches so that the investment multiplier to be greater, maybe higher of three and, therefore, the already scarce resources will have to be driven to smart investments. Also, we have predicted the change of RGDP for the next years 2009 -2012 as an output of a Neural Network, using the twelve key-factors time-functioned as inputs. This forecasting process has proved that, if the region of AMTH continues to follow the same growth strategy and to invest to the same industries the developmental result will be poor and the investment multiplier will be about 0.9-1,0. Also, the predicted RISK WEIGHTED GDP for 2008 will present a slight decrease with a tendency to be stabilized in the future. Possibly this behavior detects, qualitatively, the upcoming financial crisis. Finally, the region of EMTH has significant comparative advantages

that will have to become dynamic and competitive advantages and this requires new growth strategies. This paper, using the new approach of spatial analysis, e.g., the NM model and not the existed ones based on spatial autocorrelation statistics and the mathematical formalism of spatial interaction models, gives the tool-drivers that will take-off the regional economy.

REFERENCES Armano, G., Marchesi, M., & Murru, A. (2005). A hybrid genetic–neural architecture for stock indexes forecasting. Information Science, 170, 3–33. doi:10.1016/j.ins.2003.03.023 Baestaens, D., & Van den Bergh, M. W. (1995). Tracking the Amsterdam stock index using neural networks. In Refenes, A.-P. (Ed.), Neural networks in the capital markets (pp. 149–162). New York, NY: John Wiley & Sons.



Baro, R. J., & Sala-I-Martin, X. (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 1, 107–182. doi:10.2307/2534639 Bergerson, D., & Wunsch, K. (1993). A commodity trading model based on a neural network-expert system hybrid. In Trippi, R. R., & Turban, E. (Eds.), Neural networks in finance and investing: Using artificial intelligence to improve real-world performance (pp. 403–410). Boston, MA: Irwin. doi:10.1109/ IJCNN.1991.155192 Hanias, M., Curtis, P., & Thalassinos, J. (2007). Prediction with neural networks: The Athens stock exchange price indicator. European Journal of Economics. Finance and Administrative Sciences, 9, 21–27. Hanias, M., & Karras, D. (2009). On efficient multistep non-linear time series prediction in chaotic diode resonator circuits by optimizing the combination of non-linear time series analysis and neural networks. Engineering Applications of Artificial Intelligence, 22, 32–39. doi:10.1016/j.engappai.2008.04.016 Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feed forward networks are universal approximators. Neural Networks, 2, 359–366. doi:10.1016/0893-6080(89)90020-8

Petrakos, G., Rodriguez-Pose, A., & Rovolis, A. (2003). Growth integration and regional inequalities in Europe (Discussion Paper Series in Regional Science). London, UK: London School of Economics. Petrakos, G., & Saratsis, Y. (2000). Regional inequality in Greece. Papers in Regional Science, 76, 57–74. doi:10.1007/s101100050003 Rebelo, S. (1991). Long run policy analysis and long run growth. The Journal of Political Economy, 94, 1002–1037. Refenes, A. N., Zapranis, A., & Francis, G. (1994). Stock performance modeling using neural networks: A comparative study with regression models. Neural Networks, 7(2), 375–388. doi:10.1016/08936080(94)90030-2 Romer, P. (1986). Increasing returns and long run growth. The Journal of Political Economy, 99, 500–521. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by backpropagating errors. Nature, 323(6188), 533–536. doi:10.1038/323533a0 Sala-I-Martin. X. (1994). Regional cohesion: Evidence and theories of regional growth and convergence (Economics Working Paper No. 104). Hartford, CT: Yale University.

IGI GLOBAL PROOF

Karnin, E. D. (1990). A simple procedure for pruning back-propagation trained neural networks. IEEE Transactions on Neural Networks, 1(2), 239–245. doi:10.1109/72.80236 Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10, 169–181. doi:10.1016/0925-2312(95)00020-8 Lachtermacher, G., & Fuller, J. D. (1995). Backpropagation in time-series forecasting. Journal of Forecasting, 14, 381–393. doi:10.1002/for.3980140405 Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks, 5(6), 865–872. doi:10.1109/72.329683

O’Leary, D. E. (1998). Using neural networks to predict corporate failure. International Journal of Intelligent Systems in Accounting Finance & Management, 7, 187–197. doi:10.1002/(SICI)10991174(199809)7:33.0.CO;2-7

Tsibouris, G., & Zeidenberg, M. (1996). Testing the efficient market hypothesis with gradient descent algorithms. In Refenes, A. P. (Ed.), Neural networks in the capital markets (pp. 127–136). New York, NY: John Wiley & Sons. Velido, A., Lisboa, P. J. G., & Vanghan, J. (1999). Neural networks in business: A survey of applications (1992-1998). Expert Systems with Applications, 17, 51–70. doi:10.1016/S0957-4174(99)00016-0 Widrow, B., & Lehr, M. (1990). 30 years of adaptive neural networks: Perceptron, Madaline, and Backpropagation. Proceedings of the IEEE, 78, 1415–1442. doi:10.1109/5.58323 Wong, B. K., Bodnovich, T. A., & Selvi, Y. (1997). Neural network applications in business. A review and analysis of the literature (1988-95). Decision Support Systems, 19, 301–320. doi:10.1016/S01679236(96)00070-X



Business Process Models Representation by Deducing Interpretative Evidences on Intuitively Common Symbols Saleh Alwahaish, VŠB–Technical University of Ostrava, Czech Republic Ahmad Jaffar, United Arab Emirates University, UAE Ivo Vondrák, VŠB–Technical University of Ostrava, Czech Republic Václav Snášel, VŠB–Technical University of Ostrava, Czech Republic

ABSTRACT

IGI GLOBAL PROOF

Through quantitative analysis, previous researchers have proven a significant preference towards a specific set of notations for modeling business processes. The drawn conclusion revealed a significantly correlated coefficient preference to Norm Process Chart for using easily recognizable symbols to intuitively elicit understanding in representing business process models. Further interpretative analysis to qualitatively enhance these findings will only prove and strengthen the above claimed beyond reasonable doubt. The approach is to measure respondent level of accuracy in interpreting 3 different case studies modeled using 3 different modeling techniques shown to respondents in 3 different randomized sequences. The analysis includes correlating the finding against the time taken as well as respondents’ level of confidence in interpreting these models. The significantly correlated results again confirmed beyond reasonable doubt Norm Process Chart being respondents ultimate choice. Further comparative analysis between results from an earlier investigation against the latter, revealed similar patterns in respondents’ responses despite respondents dispersed ethnicity and educational backgrounds. Keywords:

Business Process Modeling, Common Symbols, Norm Process Chart, Quantitative Analysis, Symbolic Notations

1. INTRODUCTION Business Process Modeling (BPM), an approach to graphically display the way organizations

DOI: 10.4018/ijpmat.2012010103

conduct their business processes, has emerged as an important and relevant domain of conceptual modeling. It is considered a key instrument for the analysis and design of process-aware Information Systems (Recker et al., 2009). Business Process Model (BPM) advocates the use of symbolic notations to represent



business processes. Influenced by system engineering and mathematics, the application of these notations involves technical processes designed by engineers, undertaken by technically trained analysts for the use of largely technical people (Recker et al., 2009). However, majority of business process stakeholders are non-technically inclined with business or administrative background. While some notations are comprehensive, their arbitrary geometrical symbols can be visually and technically complex; and cognitively difficult to understand with unclear semantics. While such representational constraints prevent effective communication of process knowledge, initial hypothesis proving investigation (Rowland et al., 2003) substantiated that Norm Process Chart (NPC) (Recker et al., 2009), a newly proposed set of notations for modeling business processes, is semantically clearer than existing BPM notations. Using various statistical analysis techniques (Carter, 1997; Howitt & Cramer, 1999) the investigation revealed a linear relationship among all the variables used for this comparative analysis. With its correlation coefficient significantly at 0.1 level, not only the various results eliminate possible chance of bias but also highlighted significant pattern in favor of NPC with the mean scores (asymptotic significance at .000 for its variable grouping) for other techniques almost double as compared to NPC (Rowland et al., 2003). However, the investigation falls short at measuring confidently the level of accuracy in respondents’ interpretation of the given models. This generates a subsequent hypothesis that respondents are able to interpret processes represented using NPC more accurately than using other notations. Analysis will be based on experimental data collected through survey using different sets of respondents from the initial investigation. Using a free-format answer, respondents will be asked specific questions in relation to their interpretation of three process fragments, which will be represented by the three different notations in three different sequences. Respondents’ answers will be analyzed to measure the level of accuracy in terms of their

understandings of the given model, their level of confidence as well as time taken to appraise the given models. This paper presents an experiment designed to substantiate the developed hypothesis objectively by comparing NPC with two wellestablished approaches - Integrated Definition (Bose & Manvel, 1984; KBSI, 2000) and Roles Activity Diagrams (Ould, 1995; Holt et al., 1983). It will describe the experiment and present the results. Future work based on analyses of the outcome is also indicated.

2. DESIGNING AN INTERPRETATIVE SURVEY An experiment was proposed with the aim of comparing which notation is accurately interpreting a given model. The term accurate interpretation determines the number of correct answers to questions in interpreting a given model for representing business processes. The hypothesis asserts that there is a significant accuracy in interpretation for one notation; its opposite asserts otherwise. Using a conclusive research technique (Joppe, 2011), in the form of a questionnaire survey, respondents were asked to interpret the different process representations using Norm Process Chart (Figure 1), Role Activity Diagram (Figure 3) and Integrated DEFinition (Figure 5). These representations were applied to different process extractions (Insurance Claim, PhD Registration and Inter-Library Loan) in different randomized sequence based on Latin Square technique (Carter, 1997; Bose & Manvel, 1984). The latter produced randomized questionnaires with 36 sets of different combinations. Respondents must answer various questions for the purpose of identifying the accurate understanding of these models presented to them. The contention was to determine whether accurate interpretation for one notation in modeling business process is much higher as compared to another. Similar design for the questionnaire (Rowland et al., 2003) was adopted to maintain

IGI GLOBAL PROOF



Figure 1. NPC basic symbols (Recker et al., 2009)

Figure 2. Inter-Library loan using NPC

IGI GLOBAL PROOF consistency. The intention here is to elicit similar behavioral pattern in the statistical analysis. The proposed questions were directly testing respondents’ accuracy in interpretation based on their understanding of the process flow and logic for the given models. Respon-

dents must accurately list the various activities in its correct sequence, identifying conditional rules and parallel activities used within the process. They must also name the various participating agents and identify who triggers their interactions and finally determine their

Figure 3. RAD basic symbols (Holt et al., 1983)



Figure 4. Inter-Library loan represented using RAD

Figure 5. IDEF suite basic symbols (6)

IGI GLOBAL PROOF

Figure 6. Inter-Library loan represented using IDEF suite



Figure 7. Time taken solving questions for each model

level of confidence in their answer using a Likert Scaling technique (RMKBL, 2011).

3. RESULTS IN ANALYZING INTERPRETATIVE SURVEY The analysis must reveal a much higher level of interpretation accuracy for one notation as compared to others (either NPC, RAD or IDEF). Despite a low respond of 33% returned questionnaires, the various analysis depicted a very strong consistently significant correlated inclination towards a particular set of notation in modeling business processes. Majority of the respondents interestingly were undergraduates with minimal exposure to organizational

processes and understanding on modeling techniques. Eliminating bias results from the findings, various variables were compared against one another to assess significant correlation. The analysis compared time taken to do the questionnaire against the overall level of accuracy in interpreting the model. It also compared the latter to the level of confidence to determine respondents’ state of well-being while interpreting the models. In substantiating the accurate interpretation versus level of confidence, a detail analysis on two specific questions was also being solicited.

IGI GLOBAL PROOF

Figure 8. Accurate interpretation for IDEF method



Figure 9. Accurate interpretation for RAD method

3.1 Results: Time vs. Accurate Interpretation With reference to Figure 7, 42% of the respondents solved the NPC model questions within five minutes with 71% of them scored between 80-100% (Figures 8 through 10). As compared to RAD model, 71% of respondents took between 6-15 minutes to solve the model questions with only 39% of them scored within the 80-100%. Despite 16% of the respondents managed to complete the questionnaire within 5 minutes, IDEF fair worse with only 3% of respondents were able to interpret and answer the various questions relating to the model accurately. The

majority had great difficulties in appreciating and interpreting the various geometrical notations used to model the processes. Upon correlating time taken to solve the models’ questions to the accurate interpretation, NPC model outclass others, followed RAD and finally IDEF models.

IGI GLOBAL3.2 PROOF Results: Accuracy vs. Respondents’ Confidence

As in Figure 11, respondents’ level of confidence reflected 48% of them were 75-100% confident in solving the NPC model. Whereas only 36% and 3% expressed the same level of confidence

Figure 10. Accurate interpretation for NPC method



Figure 11. Confidence level for each model

when solving RAD and IDEF models respectively. Correlating this result against the findings on accurate interpretation, it’s matched perfectly for all the various models with NPC attaining 71% of respondents achieving more than 80% accuracy in their interpretation. Similarly to RAD and IDEF respectively, with the 45% majority in RAD attained 50-70% accuracy and

68% majority in IDEF managed the maximum of 40% accuracy. The correlation between these 3 variables (time taken, accurate interpretation and level of confidence) thus far reflected significantly consistent in pattern in preference to NPC. Not only NPC has the highest majority in the shortest time to answer the questionnaire, its differ-

IGI GLOBAL PROOF

Figure 12. Overall scores by type of process fragments



Figure 13. Overall scores by notation sequence

ences of 71% in level of accuracy almost doubled the percentage attained by RAD and IDEF combined together. Similarly, if 50% is the benchmark for level of confidence, NPC with 84% respondents attained about 20% more majorities to RAD at 68% against IDEF at 35% only. RAD as compared to IDEF held in second place consistently through.

3.3 In-depth Analysis on Specific Variables This analysis focused on accurately interpreting the notations for representing start/end of process as well as accurately determining the correct sequence of activities flow. The contention is to substantiate the analysis on overall achievement attained by the above various variables.

IGI GLOBAL PROOF

Figure 14. Overall scores by type of notation



The results depicted significantly correlated patterns in favor of NPC. Unlike IDEF, majority of respondents were able to attained accuracy level of 75% or more in identifying the notation for start/end of process. NPC with the majority of 93% depicted itself as being the easiest to be identified as compared to RAD managing at 62% only. On the contrary, Figure 13 highlighted an opposite pattern with 59% majority attained more than 75% accuracy for NPC. The RAD and IDEF majority only managed a maximum of 25% accuracy in interpreting the sequence of activity flow. RAD 29% of respondents attained above 75% accuracy while IDEF had only 7%. Nevertheless, results revealed that significantly correlated consistency attributed to the various analysis in sections A and B.

4. RESPONDENTS’ PREFERRED MODEL IS ACCURATELY INTERPRETED While previous investigation proved that NPC is much preferred notations for representing business model, the main contention here is to prove that respondents can accurately interpret processes represented using NPC more than other notations. The above analysis had proven beyond reasonable doubts that the hypothesis is true. Even with a quick glance on Figure 2 as compared to Figure 4 and Figure 6 for similar process fragment, this hypothesis stands. Upon cross-referencing each variable used for analysis, the above overwhelmingly revealed significantly correlated relationships between each of them in favor of NPC. While respondent not only took less time to appreciate and interpret NPC model for all type of process fragments in randomized sequence, their accuracy in interpretation that reflected their level of understanding of what the model presented were equally high. This is clearly confirmed with their high level of confidence in answering the questionnaire for NPC. IDEF experienced the brunt of respondents’ displeasure for its overwhelmingly technical inclination towards its representation. Not only have

most respondents unable to correctly interpret the meanings of its geometrical notations, the various leveled of abstract representation in itself confused respondents more so that RAD. Unlike NPC and IDEF being in the extreme of the equation, on the contrary, RAD overall average balances in the middle for almost all of the analysis. It reflected upon certain level of complexity within its model, where respondent have some difficulties but not overly complex as with IDEF. In comparing the findings of this investigation against the previous (Figures 12, 13, and 14), similar pattern consistency towards NPC was very evidence. All analyzed variables shown significant preference towards NPC, followed by RAD and subsequently IDEF, which mapped directly to those experienced in this analysis. It’s further proved that both hypothesis where NPC is much preferred for its readability and semantically much clearer; is also accurately interpreted with highest level of confidence within the shortest time frame. The analysis did not reveal constraint in dependence on English being the medium of communication. On the issue of ethnicity, working and educational background, where the majority of the respondents are native Arabic mostly educated in an Arabic state school, NPC projected as being more language friendly with universally common notations of arrow and statement controls as compared to RAD and worse IDEF. Similarly, NPC also could be projected as being less technically inclined. This was derived from the fact that majority of respondents were fresh undergraduates neither with much organizational and business process experiences nor exposure to modeling technique except for Dataflow Diagram as part of their elementary undergraduate studies. The fact that they formed the bulk of respondents and yet achieved high percentage of accurate interpretation confidently within a short time frame, highlighted the unnecessary technical exposure needed to decipher NPC models. Ironically, RAD and IDEF being more technically influenced; attained reversed scores against them as compared to NPC.

IGI GLOBAL PROOF



5 CONCLUSION This research hypothesizes that certain notation can be interpreted much more accurately as compared to other notations. It has also proved that there exists pattern consistency in its findings with a previous investigation (Rowland et al., 2003) and that ethnicity and educational background has no bearing on its outcome. The hypothesis had been proven in favor of NPC both in terms of quantitative evidences from the previous investigation as well as the above interpretative research findings. Not only NPC co-efficiently and significantly correlated in respondents’ preference in the initial investigation but also proven to be accurately interpreted by different groups of dispersed origin and educational backgrounds as in the above analysis. Unlike the correlated variables of accurate interpretation versus time and level of confidence, there were weak comparative analysis on proving the pattern consistency and respondents’ background. Nevertheless, NPC had been accorded with being the most preferred as well as easily and accurately interpreted as compared to RAD and IDEF. For the sake of clarity, a comparative analysis of NPC and Unified Modeling Language Activity Diagrams (UML AD) is listed in the Appendix. Other analysis method must be advocated to address the identified weak comparative analysis. While the respondents’ population is subject to scrutiny, extending the survey to include various industrial partners will strengthen the findings into proving NPC usability, readability and perhaps weaknesses as well. In discovering such favorable results towards NPC prompt further explanation on its achievement. With the use of developed tools such as GATE within the realm of Natural Language Processing will be useful to further emphasize the consistency in accurate interpretation via techniques such as

text parsing and pattern matching in relation to respondents’ textual answers to various survey questions.

REFERENCES Bose, R., & Manvel, B. (1984). Introduction to combinatorial theory. New York, NY: John Wiley & Sons. Carter, D. (1997). Doing quantitative psychological research: From design to report. Sussex, UK: Psychology Press. Holt, A., Ramsey, H., & Grimes, J. (1983). Coordination system technology as the basis for a programming environment. ITT Technical Journal, 57(4), 307–314. Howitt, D., & Cramer, D. (1999). A guide to computing statistics with SPSS for Windows - Version 8. Upper Saddle River, NJ: Prentice Hall. Indulska, M., Recker, J., Rosemann, M., & Green, P. (2009). Business process modeling: Current issues and future challenges. In P. van Eck, J. Gordijn, & R. Wieringa (Eds.), Proceedings of the International Conference on Advanced Information Systems Engineering (LNCS 5565, pp. 501-514).

IGI GLOBAL PROOF

Jaffar, S., Rowland, H., Alderson, A., & Flecther, M. (2003). An empirical comparison of some business processes notations. In Proceedings of the International Conference on Information and Knowledge Engineering, Las Vegas, NV (pp. 344-350). Joppe, M. (2011). The research process. Retrieved from http://www.htm.uoguelph.ca/MJResearch/ ResearchProcess/default.html KBSI. (2000). A structured approach to enterprise modeling and analysis. Retrieved from http://www. idef.com/default.html Ould, M. (1995). Business process modelling and analysis for reengineering and improvement. New York, NY: John Wiley & Sons. Trochim, W. K. (2006). Research methods knowledge base: Likert scaling. Retrieved from http://www. socialresearchmethods.net/kb/scallik.php



APPENDIX Table 1A. Process Concept

NPC notation

NPC semantics

UML AD notation

UML AD semantics

Activity

A Label denotes the activity name.

An activity expressed by the verb and noun in the activity name

A rectangle with rounded ends with the activity name inside.

An activity expressed by the verb and noun in the activity name.

State

An arrow Connecting activities.

Denotes a state or sub state of a role.

A rectangle with rounded corners.

Denotes a state. A state may change to another. An activity may end and lead to another activity. An activity may end and lead to a state. A state may change and lead to an activity.

Transitions

An arrow from activity to activity.

An activity may end and lead to another activity.

An arrow from state to state, activity to activity, state to activity, or activity to state.

Start state

Not supported.

Not applicable.

A big, black dot

Denotes the initial state of a process

Stop state

Not supported.

Not applicable.

A big black dot with a circle around it.

Denotes an end state of a process.

External event

An arrow

Denotes an external event

Control icon

Denotes an external event

Sequencing activities

An activity label A1 followed by an arrow to another Activity label A2

Denotes that activity A1 will be followed by activity A2

An open box with rounded ends A1 followed by an arrow to another A2

Denotes that activity A1 is followed by activity A2

Looping

An arrow that loops back to an earlier activity.

Iteration of activities.

An arrow that loops back to an earlier state or activity.

Iteration of activities and/ or states.

Role

A Rectangle with the role name in a black box on top containing activities, interactions, states, etc.

An organizing unit for bounding activities (and states) that are strongly related.

Swim lanes containing activities, states, transitions, etc.

An organizing unit for bounding activities (and states) that are strongly related.

State test

A question testing the state, and multiple arrows coming out labeled with possible answers.

Depending on the answer to the question only one out of more than one thread will be followed.

1. An arrow into a diamond with up to three arrows coming out, each labeled with a possible state. 2. Multiple arrows from either state or an activity where each arrow is labeled.

Depending on which state is true only one out of more than one thread will be followed. Depending on which label is valid only one thread will be followed.

Concurrent threads

Two or more arrows issuing from a “state line”; each starts a new “sub state line” connected with an “&” labeled black box.

Denotes a series of parallel threads of activities. Activities may be interleaved or performed simultaneously.

Two or more arrows issuing from synchronization bar.

Denotes a series of parallel threads of activities and states. Activities may be interleaved or performed simultaneously.

Replication of threads

Not supported.

Not applicable.

An asterisk beside an arrow labeled with a repetition statement.

The activities below will performed in parallel a number of times.

Synchronize threads

Multiple arrows arrive at synchronization into one line.

The threads represented by the arrows are synchronized.

Multiple arrows arrive at a synchronization bar.

The threads represented by the arrows are synchronized.

Interaction between roles

An arrow between two or more activities in different roles.

Denotes synchronous interaction between activities in two different roles.

Not explicitly supported.

Not applicable.

IGI GLOBAL PROOF



Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method Golam Kabir, University of British Columbia, Canada Razia Sultana Sumi, \Stamford University Bangladesh, Bangladesh

ABSTRACT Evaluation and selection of a concrete production facility location is an important strategic decision making problems for both the public and private sector. The multi-dimensional, multi-criteria nature of the concrete production facility location problem limits the usefulness of any particular single objective model. In this study, social, economical, technological, environmental, and transportation factors and sub criteria have been derived to make the optimal concrete production facility location selection decision more realistic and effectual. This study shows an improved and appropriate concrete production facility location evaluation and selection model has been developed by integrating Modified Delphi and Fuzzy Analytic Hierarchy Process (FAHP) with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. An example is presented to show applicability and performance of the proposed methodology followed by a sensitivity analysis to discuss and explain the results.

IGI GLOBAL PROOF

Keywords:

Concrete Production Facility Location, Delphi Method, Fuzzy Analytic Hierarchy Process (AHP), Multi-Criteria Analysis, Sensitivity Analysis, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) Method

1. INTRODUCTION From a low cost and rather unusual material in the late nineteenth century, concrete became “the stone” of the twentieth. From construction elements to urban furniture, a large variety of concrete objects surround us nowadays. Concrete is one of the most widely used building materials in roads, buildings, bridges and other infrastructures. On average, approximately 1 ton of concrete is produced each year for every DOI: 10.4018/ijpmat.2012010104

human being in the world (Lippiatt and Ahmad, 2004). When compared to other construction materials, concrete has more strength, ease of product and ease of maintenance. Since more than 150 years, research on cement concrete have contributed to improve its mechanicals (strength, durability) and casting (self-compacting.) characteristics (Cazacliu and Ventura, 2010). With these characteristics, concrete is the most common construction material in the world as well as in Bangladesh. In industrial countries the number of constructions is growing as a result of urbanization and industrial



investment. And concrete is preferred as a construction material increasingly. In recent years, to meet the construction needs concrete production facilities have been built in different areas. The problem of facility location is common to all businesses. The strategic planning of facility location is critical to a company’s eventual success. A suitable location can provide favorable contributions to a company’s market competitiveness. More and more firms are clearly dispersing parts of their production process to locations around the world to take advantage of national differences in the cost and quality of labor, talent, energy, facilities and capital (Partovi, 2006). The decision of the location selection of concrete production facility is a crucial aspect as it depends on multiple factors or constraints like economical, environmental, social, political, technological etc. (Cazacliu and Ventura, 2010; Chowdhurya, Apul, and Fry, 2010; Rajendran and Gambatese, 2007; Turhan et al., 2010; Zapata and Gambatese, 2005). In this complex and conflicting situation, a multi criteria analysis (MCA) is inevitable for locating concrete production facility where both quantitative and qualitative objectives can be considered. Real-life applications of multicriteria decisionmaking methods require the processing of imprecise, uncertain, qualitative or vague data (Joshi, Banwet, and Shankar, 2011; Kabir and Hasin, in press; Rajesh, Pugazhendhi, and Ganesh, 2009). Therefore the main objective of this research is to formulate a decision support model (system), which would facilitate in the decision making and to evaluate the best concrete production facility location among a set of discrete potential locations. An improved and more accurate concrete production facility location evaluation and selection model has been developed by integrating modified Delphi method and Fuzzy AHP with TOPSIS method. The Delphi method is an iterative process used to collect and distill the judgments of experts using a series of questionnaires interspersed with feedback. Therefore, it de-livers qualitative as well as quantitative results and has beneath its explorative, predictive even

normative elements. The Delphi technique is an appropriate methodology for identifying the significant factors and issues for the evaluation and selection of power substation location (Rajesh, Pugazhendhi, and Ganesh, 2011; Vidal, Marle, and Bocquet, 2011). Fuzzy logic (FL) is a method for understanding, quantifying and dealing with vague, ambiguous and uncertain characteristics, ideas and judgments (Kabir and Hasin, 2011; Rajesh et al., 2009). Fuzzy AHP is being used more and more frequently in multi-criteria decision- making because of its simplicity and similarity to human reasoning. This method is suitable for use in evaluating policies (including tangible and intangible information). Further- more the FAHP also allows group decision-making to derive priorities based on sets of pairwise comparisons (Kabir and Hasin, 2012). TOPSIS technique is based on the concept that the ideal alternative has the best level for all attributes considered, whereas the negative-ideal is the one with all the worst attribute values. A closeness coefficient is defined to determine the ranking order of alternatives which is simultaneously the farthest from the negative-ideal and the closest to the ideal alternative (Joshi, Banwet, and Shankar, 2011; Kutlu and Ekmekçioglu, 2012; Rajesh, Pugazhendhi, and Ganesh, 2009). The remainder of this paper is organized as follows. In the next section, the proposed methodology has been described with brief note on modified Delphi method, fuzzy AHP technique and TOPSIS method. The proposed methodology is applied to evaluate and select the optimal location of a concrete production facility in Bangladesh in the following section. Finally, the last section presents the conclusion and discusses the limitations and scope for future research.

IGI GLOBAL PROOF

2. THE PROPOSED METHODOLOGY Fuzzy multiple attribute decision-making (FMADM) methods have been developed owing to the imprecision in assessing the relative



importance of attributes and the performance ratings of alternatives with respect to attributes. Imprecision may arise from a variety of reasons: unquantifiable information, incomplete information, unobtainable information and partial ignorance. Conventional MADM methods cannot effectively handle problems with such imprecise information. To resolve this difficulty, fuzzy set theory, first introduced by Zadeh, has been used and is adopted herein. Fuzzy set theory attempts to select, prioritize or rank a finite number of courses of action by evaluating a group of predetermined criteria. Solving this problem thus requires constructing an evaluation procedure to rate and rank, in order of preference, the set of alternatives. In this study AHP as a MADM technique used with fuzzy logic together. The weights that are gained from fuzzy-AHP calculations are considered and used in TOPSIS calculations. It must be emphasized that the weights of fuzzyAHP is gained by modified Delphi method. Then TOPSIS is applied for the evaluation problem and the results of it show the preference order of the concrete production facility selection problem. This methodology levels can be seen in Figure 1 clearly. The levels of the methodology are detailed theoretically in following subsections.

reached on a particular topic (Chang, Wu, and Chen, 2008; Joshi, Banwet, and Shankar, 2011). The decision-making group probably should not be too large, i.e., a minimum of five to a maximum of about 50 (Gumus, 2009). Murry and Hammons (1995) suggested that the modified Delphi method summarize expert opinions on a range from 10 to 30 (Chang, Wu, and Chen, 2008). So, in this study, 12 experts from construction engineering, logistics, technical, corporate, finance departments and academic background participated in the modified Delphi method-based decision group.

2.2. Fuzzy AHP A good decision-making model needs to tolerate vagueness or ambiguity because fuzziness and vagueness are common characteristics in many decision-making problems (Kabir and Hasin, 2011; Saleeshya, Babu, and Vishnu, 2011). Since decision-makers often provide uncertain answers rather than precise values, the transformation of qualitative preferences to point estimates may not be sensible. Conventional AHP that requires the selection of arbitrary values in pairwise comparison may not be sufficient, and uncertainty should be considered in some or all pairwise comparison values (Gumus, 2009; Kabir and Hasin, in press; Saleeshya and Babu, 2011). Since the fuzzy linguistic approach can take the optimism/pessimism rating attitude of decision-makers into account, linguistic values, whose membership functions are usually characterized by triangular fuzzy numbers, are recommended to assess preference ratings instead of conventional numerical equivalence method (Liang and Wang, 1994). As a result, the fuzzy-AHP should be more appropriate and effective than conventional AHP in real practice where an uncertain pairwise comparison environment exists (Kutlu and Ekmekçioglu, 2012; Rajesh, Pugazhendhi, and Ganesh, 2009; Sen and Cinar, 2010). Decision-makers usually find that it is more confident to give interval judgments than fixed value judgments. This is because usually he/she is unable to explicit about

IGI GLOBAL PROOF

2.1. Modified Delphi Method The Delphi method accumulates and analyzes the results of anonymous experts that communicate in written, discussion and feedback formats on a particular topic. Anonymous experts share knowledge skills, expertise and opinions until a mutual consensus is achieved (Chang, Wu, and Chen, 2008; Hsu, Wu and Li, 2008; Rajesh, Pugazhendhi, and Ganesh, 2011; Vidal, Marle, and Bocquet, 2011). The Delphi method consists of five procedures: (1) select the anonymous experts; (2) conduct the first round of a survey; (3) conduct the second round of a questionnaire survey; (4) conduct the third round of a questionnaire survey; and (5) integrate expert opinions and to reach a consensus. Steps (3) and (4) are normally repeated until a consensus is



Figure 1. The levels of optimal power substation location selection

IGI GLOBAL PROOF

his/her preferences due to the fuzzy nature of the comparison process (Kabir and Hasin, 2012). There are the several procedures to attain the priorities in FAHP. The fuzzy least square method (Xu, 2000), method based on the fuzzy modification of the logarithmic least squares method (Boender, de Graan, and Lootsma, 1989), geometric mean method (Buckley, 1985), the direct fuzzification of the method of Csutora and Buckley (2001), synthetic extend analysis (Chang, 1996), Mikhailov’s fuzzy preference programming (Mikhailov, 2003) and two-stage logarithmic programming (Wang, Yang, and Xu, 2005) are some of these methods. Chang’s extent analysis is utilized in this research to evaluate the focusing problem.

Chang (1996) develops a new approach for handling fuzzy AHP, using triangular fuzzy numbers (TFN) for the pairwise comparison scale of fuzzy AHP, and using the extent analysis method for the synthetic extent values of the pairwise comparisons. A TFN denoted as M = (l, m, u) where l < m < u, has the following triangular type membership function: 0,  (x − l ) / (m − l ) µM (x ) =  (u − x ) / (u − m )  0, 

x u,

(1)



Table 1. Linguistic terms and corresponding triangular fuzzy numbers Fuzzy Number

Positive triangular scale (l, m, u)

9□-1

(1/9,1/9,1/9)

Intermediate values between 7□ and 9□

-1

8□

(1/9,1/8,1/7)

Very unimportance

7□

-1

(1/8,1/7,1/6)

Intermediate values between 5□-1 and7□-1

6□-1

(1/7,1/6,1/5)

Essential unimportance

5□

(1/6,1/5,1/4)

Linguistic term Extreme unimportance -1

-1

-1


-1

4□

(1/5,1/4,1/3)

Moderate unimportance

3□-1

(1/4,1/3,1/2)


2□

(1/3,1/2,1)

Equally importance

1□

(1,1,1)


2□

(1,2,3)

Moderate importance

3□

(2,3,4)


4□

(3,4,5)

Essential importance

5□

(4,5,6)


6□

(5,6,7)

Very vital importance

7□

(6,7,8)


8□

(7,8,9)

Extreme unimportance

9□

(9,9,9)

-1

-1

-1

-1

IGI GLOBAL PROOF

In this methodology, decision makers are asked to indicate the relative importance of two evaluation criteria in the same level. The scores of pairwise comparison are treated as linguistic variables, which are represented by positive TFNs as illustrated in Table 1. After obtaining the fuzzy judgment matrices, extent analysis method and the principle of TFNs comparison are used to derive the importance weights of criteria from pairwise comparisons. Let W be the normalized weight vector of triangular fuzzy comparison matrix A, which includes the importance weights of criteria in the crisp form. The steps for calculating this vector are as follows. The first step is to calculate the fuzzy synthetic extent value of each pairwise comparison. Linguistic pairwise comparisons are transformed to corresponding TFNs illustrated in Table 1. Denote C□xy as the TFN related to

the pairwise comparison of criterion x over criterion y, which is represented as (lxy, mxy, uxy). According to Chang (1996), the value of fuzzy synthetic extent with respect to the criterion x, denoted as S□x = (lx, mx, ux), can be obtained via Eq. (2): n

S X = ∑ C xy y =1

1

 n n   ⊗  ∑ ∑ C xy  x = 1, 2,..., n    k =1 y =1 (2)

where n is the size of the fuzzy judgment matrix A. n  , perform the fuzzy To obtain ∑ y =1C xy addition operation such that n

∑C y =1

xy

n n  n  = ∑ lxy , ∑ mxy , ∑ uxy  x = 1, 2,...,, n   y =1 y =1 y =1 (3)



(

)

(

)

V S ≥ S x = hgt S x ∩ S y       1, if my ≥ mx    = µsy (d ) = 0, if lx ≥ uy  n n n n n n  n n    ax − cy  ∑ ∑C ky ∑ ∑ lky , ∑ ∑ mky , ∑ ∑ uky   , otherwise  k =1 y =1 k =1 y =1 k =1 y =1 k =1 y =1  by − cy ) − (bx − ac ) (    (4) (7) 1

n  n   , perform the and to obtain  ∑ k =1 ∑ y =1C  ky   fuzzy addition operation of C□ky values such that

and then compute the inverse of the vector in Eq. (4) by using Eq. (5):

where d is the ordinate of the highest intersection point D between µsx and µsy . To compare

S.x and S.y, we need both the values of V(S.y ≥ S.x) and V(S.x ≥ S.y). However, according to Wang, Luo, and Hua (2007), the degree of possibility defined by the extent analysis method is an index for compar(5) ing two triangular fuzzy numbers rather than an index for calculating their relative importance. According to Wang, Luo, and Hua (2007), Therefore, normalized degrees of possibility can Eq. (5) can be corrected as Eq. (6) only show to what degree a triangular fuzzy number is greater than all the others, but cannot   be used to represent their relative importance.   1   This problem can be well resolved by using n n n   the total integral value with index of optimism  ∑ y =1 lky + ∑ k =1,k ≠x ∑ y =1 uky  1  n n    1 developed by Liou and Wang (1992), which      ,   ∑ ∑ C  = , n n  derives the priorities of the synthetic extent  k =1 y =1 ky   ∑ k =1 ∑ y =1 mky   values of A by the following equation:   1 1

 n n    ∑ ∑ C ky      k =1 y =1     1 1 1 =  n , ,  n n n n n   u m l  ∑ k =1 ∑ y =1 ky ∑ k =1 ∑ y =1 ky ∑ k =1 ∑ y =1 ky 

IGI GLOBAL PROOF

   n  n n  ∑ y =1 uky + ∑ k =1,k ≠x ∑ y =1 lky 

(6)

The second step is to derive fuzzy ranking value of S□x. In this step, S□x is compared to other synthetic extent values of A, S□y = (ly, my, uy). According to Chang (1996), the degree of possibility of S□x ≥ S□y is obtained by the following equation:

( )

1 ITα S x = α (mx + ux ) 2 1 + (1 − α) (lx + mx ) 2 1 = αux + mx + (1 − α)lx   2

(8)

where α is index of optimism which represents degree of optimism for decision makers. If α approaches 0 in [0, 1], the decision makers are more pessimistic and otherwise they are more optimistic (Sen and Cinar, 2010).



Finally, define the normalized importance weight vector W = (w1, w2,…., wn)T of the fuzzy judgment matrix A by using following equation:

wx =

( ) x = 1, 2,..., n ∑ I (S ) ITα S x n

α k =1 T

(9)

x

values attainable of criteria, whereas the negative ideal solution consists of all worst values attainable of criteria (Ertuğrul and Karakasoğlu, 2009; Kutlu and Ekmekçioglu, 2012; Taskin, 2008). The method is calculated as follows: Step 1: Construct normalized decision matrix.

where wx is a non-fuzzy number. After comparison is made, it is necessary to check the consistency ratio of the comparison. To do so, the graded mean integration approach is utilized for defuzzifying the matrix. According to the graded mean integration approach, a fuzzy number L□x = (l1, l2,l3) can be transformed into a crisp number by employing the below equation:

This step transforms various attribute dimensions into non-dimensional attributes, which allows comparisons across criteria. Normalize scores or data as follows:

 = L = l1 + 4l2 + l 3 P L 6

Step 2: Construct the weighted normalized decision matrix.

()

(10)

After the defuzzification of each value in the matrix, ‘consistency ratio’ (CR) of the matrix can easily be calculated and checked whether CR is smaller than 0.10 or not (Kutlu and Ekmekçioglu, 2012).

ri, j =

x ij

∑

m

, i = 1, 2,...., m; j = 1, 2,..., n

x i =1 ij

(11)

Assume a set of weights for each criteria is wj for j = 1,…, n. Multiply each column of the normalized decision matrix by its associated weight. An element of the new matrix is:

IGI GLOBAL PROOF

2.3. TOPSIS Method TOPSIS is one of the useful Multi Attribute Decision Making techniques that are very simple and easy to implement, so that it is used when the user prefers a simpler weighting approach. TOPSIS method was firstly proposed by Hwang and Yoon (1981). According to this technique, the best alternative would be the one that is nearest to the positive ideal solution and farthest from the negative ideal solution (Parsaei et al., 2012; Wang and Chang, 2007;). The positive ideal solution is a solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution maximizes the cost criteria and minimizes the benefit criteria (Wang and Lee, 2007; Lin et al., 2008; Joshi, Banwet, and Shankar, 2011; Rajesh, Pugazhendhi, and Ganesh, 2009; Taskin, 2008). In other words, the positive ideal solution is composed of all best

vij = wj rij, for i = 1, …, m; j = 1, …, n

(12)

Step 3: Determine the positive ideal and negative ideal solutions. Positive Ideal solution: A* = { v1*, …, vn*}, where vj* ={ max (vij) if j ∈ J ; min (vij) if j ∈ J’ }

(13)

Negative ideal solution: A’ = { v1’, …, vn’ }, where v’ = { min (vij) if j ∈ J ; max (vij) if j ∈ J’ }

(14)

Step 4: Calculate the separation measures for each alternative, using the n-dimensional Euclidean distance.



The separation from the ideal alternative is: d1+ =

{∑

n j =1

(

vij − v j+

1 2

)} 2

, i = 1, 2,..., m (15)

Similarly, the separation from the negative ideal alternative is:  n di = ∑ j =1 vij − v j 

(

)

2

1

2  , i = 1, 2,..., m 

(16)

Step 5: Calculate the relative closeness to the ideal solution Ci*. The relative looseness of the alternative Ai with respect to A* is defined as C i* =

(d

di + i

+ di

)

, i = 1, 2,..., m

(17)

by using modified Delphi method. After that a two step fuzzy-AHP and TOPSIS methodology is proposed to realize the evaluation. Via considering these criteria, the weights of five location alternatives are calculated by using fuzzy-AHP, and these calculated weight values are used as TOPSIS inputs. Then, after TOPSIS calculations, evaluation of the alternatives and selection of the most appropriate location is realized. At the end of this section, the methodology structure and results are analyzed in detail by sensitivity analysis.

3.1. Evaluation Procedure The two step methodology proposed here can be run realizing the levels explained. Modified Delphi method: Initially a literature review was conducted to identify various criteria and sub criteria considered by location researchers and decision makers. Thereafter, these factors were placed in front of the selected group consisting of 12 experts to elicit the most appropriate and widely considered criteria for CPFL. Once the evaluation criteria were defined, the experts group generate a decision hierarchy model (Figure 2), having a high potential for applicability in national/international CPFL decision making. The objective or the overall goal of the decision is presented at the top level of hierarchy. Specifically, the overall goal of this application is to ‘Optimal concrete production facility location selection’. The second and third levels represent criteria and sub-criteria for selection of CPFL. Finally, at the lowest level of the hierarchy, the location alternatives of the concrete production facility are identified, which are decision options. Demographic study analysis had narrowed down the proposed locations to five different localities by the experts. Those are RST, OMR, IRT, DOS and CLS which are the first letters of the respective location alternatives. Fuzzy-AHP: After the decision hierarchy model was defined, the proposed approach in the earlier section was applied. In fuzzy-AHP, firstly, the criteria and alternatives’ importance weights must be compared. For this reason,

IGI GLOBAL PROOF

Since di ≥ 0 and di+ ≥ 0, then clearlyC i* є [0,1]

Step 6: By comparing CCi* values, the ranking of alternatives are determined. For ranking alternatives using this index, alternatives can be ranked in decreasing order. The basic principle of the TOPSIS method is that the chosen alternative should have the “shortest distance” from the positive ideal solution and the “farthest distance” from the negative ideal solution.

3. APPLICATION OF THE PROPOSED METHODOLOGY The proposed methodology is applied to evaluate and select the optimal concrete production facility location (CPFL) in the northern part of Bangladesh. For this reason, first of all, the main criteria and sub criteria to evaluate and select the optimal CPFL has been determined



Figure 2. Decision hierarchy model for concrete production facility location selection

IGI GLOBAL PROOF

there must be linguistic terms and their equivalent fuzzy numbers denoting comparison measures. The linguistic comparison terms and their equivalent fuzzy numbers considered in this paper are shown in Table 1. Afterwards, for the first step, the comparisons about the criteria and alternatives, and the weight calculation need to be made. Thus, the evaluation of the criteria according to the main goal and the evaluation of the alternatives for these criteria must be realized. Then, after all these evaluation procedure, the weights of the alternatives can be calculated. In the second step, these weights are used to TOPSIS calculation for the final evaluation. The evaluation team was asked to make pairwise comparisons for the main and subcriteria using scales shown in Table 1. The geometric mean of the individual assignments could be used to obtain overall results, however, in this case, team members reached consensus on the evaluations with the help of the Delphi

Method. Hence, a single evaluation represents the team’s opinion. The comparison matrix for the criteria with respect to the overall goal or objective can be seen from Table 2. The weight calculation details using Table 2 are given. At the beginning of the calculation, it is necessary to check the consistency ratio of the comparison matrix using Eq. 10. The consistency ration of comparison matrix (Table 2) is 0.044759. As CR < 0.1 the level of inconsistency present in the information stored in comparison matrix is satisfactory. Because of the other calculations are similar for each comparison matrix, these are not given here and can be done simply according the computations below. The value of fuzzy synthetic extent with respect to the ith object (i = 1,2, . . ., 5) is calculated as SSOF = (15, 18, 21) ⨂ [1/(15+40.143), 1/52.65, 1/(21+29.353)] = (0.272, 0.342, 0.417)



Table 2. Pairwise comparison matrix and relative weights with respect to goal Goal

SOF

TEF

ECF

ENF

SIC

Relative Weights

SOF

1,1,1

1,1,1

2,3,4

4,5,6

7,8,9

0.340

ECF

1,1,1

1,1,1

1,1,1

4,5,6

6,7,8

0.285

TEF

1/4,1/3,1/2

1,1,1

1,1,1

4,5,6

4,5,6

0.234

ENF

1/6,1/5,1/4

1/6,1/5,1/4

1/6,1/5,1/4

1,1,1

3,4,5

0.107

TRF

1/9,1/8,1/7

1/8,1/7,1/6

1/6,1/5,1/4

1/5,1/4,1/3

1,1,1

0.034

SECF = (13, 15, 17) ⨂ [1/(13+44.143), 1/52.65, 1/(17+31.353)] = (0.227, 0.285, 0.352) STEF = (10.25, 12.33, 14.5) ⨂ [1/(10.25+46.643), 1/52.65, 1 /(14.5+34.103)] = (0.180, 0.234, 0.298) SENF = (4.5, 5.6, 6.75) ⨂ [1/(4.5+54.393), 1/52.65, 1/(6.75+39.853)] = (0.076, 0.106, 0.145) STRF = (1.603, 1.72, 1.893) ⨂ [1/(1.603+59.25), 1/52.65, 1/(1.893+42.75)] = (0.026, 0.033, 0.042)

ITEF = 0.23650; IENF = 0.10825; ITRF = 0.0335 After obtaining the total integral values by using Eq. (8), the importance weights of the main criteria were obtained via normalization (Eq. (9)): W = (0.340, 0.285, 0.234, 0.107, 0.034) Now, the comparison matrix for the subcriteria and the calculated weights are given in Table 3 through Table 7. The alternative locations must be evaluated with respect to each sub-criterion. The evaluation of the alternatives with respect to the criteria and the calculated weights are given in Table 8 through Table 20.

IGI GLOBAL PROOF

The integral values of these values were calculated as below (Eq. (8)): ISOF = 0.34325; IECF = 0.28725;

Figure 3. Model results changes caused by the sensitivity analysis



Table 3. Pair wise comparison matrix and relative weights w.r.t social factors Social Factors (SOF)

LRC

SPC

Relative Weights

LRC

1,1,1

2,3,4

0.742

SPC

1/4,1/3,1/2

1,1,1

0.258

Table 7. Pair wise comparison matrix and relative weights w.r.t transportation factors Transportation Factors (TRF)

IOT

ATW

RAS

Relative Weights

IOT

1,1,1

1/6,1/5,1/4

1/4,1/3,1/2

0.10

ATW

4,5,6

1,1,1

3,4,5

0.628

RAS

2,3,4

1/5,1/4,1/3

1,1,1

0.272

Table 8. Pair wise comparison matrix and relative weights w.r.t legal and regulatory compliance Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

9,9,9

3,4,5

2,3,4

5,6,7

0.453

OMR

1/9,1/9,1/9

1,1,1

1/9,1/8,1/7

1/3,1/2,1

1/3,1/2,1

0.049

IRT DOS CLS

IGI GLOBAL PROOF 1/5,1/4,1/3

7,8,9

1,1,1

1,2,3

2,3,4

0.281

1/4,1/3,1/2

1,2,3

1/3,1/2,1

1,1,1

2,3,4

0.139

1/7,1/6,1/5

1,2,3

1/4,1/3,1/2

1/4,1/3,1/2

1,1,1

0.078

Table 20. Pair wise comparison matrix and relative weights w.r.t reachable area size Locations

RST

OMR

IRT

DOS

Relative Weights

CLS

RST

1,1,1

9,9,9

5,6,7

1,2,3

6,7,8

0.427

OMR

1/9,1/9,1/9

1,1,1

1/7,1/6,1/5

1/9,1/8,1/7

1/6,1/5,1/4

0.028

IRT

1/7,1/6,1/5

5,6,7

1,1,1

1/3,1/2,1

1,2,3

0.168

DOS

1/3,1/2,1

7,8,9

1,2,3

1,1,1

4,5,6

0.257

CLS

1/8,1/7,1/6

4,5,6

1/3,1/2,1

1/6,1/5,1/4

1,1,1

0.120

Table 4. Pair wise comparison matrix and relative weights w.r.t econimical factors Economical Factors (ECF)

IC

TC

LC

Relative Weights

IC

1,1,1

1/5,1/4,1/3

1,1,1

0.148

TC

3,4,5

1,1,1

4,5,6

0.710

LC

1,1,1

1/6,1/5,1/4

1,1,1

0.142



Table 5. Pair wise comparison matrix and relative weights w.r.t technological factors Technological Factors (TEF)

CWP

PD

Relative Weights

CWP

1,1,1

2,3,4

0.742

PD

1/4,1/3,1/2

1,1,1

0.258

Table 6. Pair wise comparison matrix and relative weights w.r.t environmental factors Environmental Factors (ENF)

SWT

TH

NH

Relative Weights

SWT

1,1,1

4,5,6

5,6,7

0.703

TH

1/6,1/5,1/4

1,1,1

1,2,3

0.191

NH

1/7,1/6,1/5

1/3,1/2,1

1,1,1

0.106

Table 9. Pair wise comparison matrix and relative weights w.r.t socio-political conditions Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

7,8,9

2,3,4

2,3,4

4,5,6

0.341

OMR

1/9,1/8,1/7

1,1,1

1/9,1/8,1/7

1/8,1/7,1/6

1/5,1/4,1/3

0.058

IGI GLOBAL PROOF

IRT

1/4,1/3,1/2

7,8,9

1,1,1

1,2,3

5,6,7

0.297

DOS

1/4,1/3,1/2

6,7,8

1/3,1/2,1

1,1,1

2,3,4

0.205

CLS

1/6,1/5,1/4

3,4,5

1/7,1/6,1/5

1/4,1/3,1/2

1,1,1

0.099

Table 10. Pair wise comparison matrix and relative weights w.r.t investment cost Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

1/4,1/3,1/2

3,4,5

1/7,1/6,1/5

1/5,1/4,1/3

0.109

OMR

2,3,4

1,1,1

4,5,6

1/6,1/5,1/4

1/3,1/2,1

0.182

IRT

1/5,1/4,1/3

1/6,1/5,1/4

1,1,1

1/9,1/8,1/7

1/7,1/6,1/5

0.038

DOS

5,6,7

4,5,6

7,8,9

1,1,1

2,3,4

0.423

CLS

3,4,5

1,2,3

5,6,7

1/4,1/3,1/2

1,1,1

0.248

Finally, the local and global weights of the criteria and the relative weights of the alternatives are determined and shown in Table 21. TOPSIS Method: The weights of the alternatives are calculated by fuzzy- AHP up to now, and then these values can be used in TOP-

SIS. So, the TOPSIS methodology must be started at the second step (the steps of the TOPSIS are given in the previous sections). Table 22 shows the normalized weighted decision matrix for each alternative with respect to the each sub-criterion.



Table 11. Pair wise comparison matrix and relative weights w.r.t transportation cost Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

4,5,6

7,8,9

1/5,1/4,1/3

3,4,5

0.343

OMR

1/6,1/5,1/4

1,1,1

1,2,3

1/7,1/6,1/5

1,2,3

0.104

IRT

1/9,1/8,1/7

1/3,1/2,1

1,1,1

1/8,1/7,1/6

1/3,1/2,1

0.047

DOS

3,4,5

5,6,7

6,7,8

1,1,1

4,5,6

0.430

CLS

1/5,1/4,1/3

1/3,1/2,1

1,2,3

1/6,1/5,1/4

1,1,1

0.076

Table 12. Pair wise comparison matrix and relative weights w.r.t logistics cost Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

7,8,9

3,4,5

1,2,3

4,5,6

0.375

OMR

1/9,1/8,1/7

1,1,1

1/6,1/5,1/4

1/9,1/8,1/7

1/6,1/5,1/4

0.031

IRT

1/5,1/4,1/3

4,5,6

1,1,1

1/4,1/3,1/2

1,2,3

0.164

DOS

1/3,1/2,1

7,8,9

2,3,4

1,1,1

2,3,4

0.293

CLS

1/6,1/5,1/4

4,5,6

1/3,1/2,1

1/4,1/3,1/2

1,1,1

0.136

IGI GLOBAL PROOF

Table 13. Pair wise comparison matrix and relative weights w.r.t cement and water potential Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

9,9,9

5,6,7

6,7,8

1,2,3

0.411

OMR

1/9,1/9,1/9

1,1,1

1/7,1/6,1/5

1/8,1/7,1/6

1/8,1/7,1/6

0.026

IRT

1/7,1/6,1/5

5,6,7

1,1,1

1,2,3

1/4,1/3,1/2

0.158

DOS

1/8,1/7,1/6

6,7,8

1/3,1/2,1

1,1,1

1/5,1/4,1/3

0.149

CLS

1/3,1/2,1

6,7,8

2,3,4

3,4,5

1,1,1

0.256

Table 14. Pair wise comparison matrix and relative weights w.r.t potential demand Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

1/6,1/5,1/4

1,2,3

1/9,1/8,1/7

1/8,1/7,1/6

0.060

OMR

4,5,6

1,1,1

6,7,8

2,3,4

1/3,1/2,1

0.278

IRT

1/3,1/2,1

1/8,1/7,1/6

1,1,1

1/9,1/8,1/7

1/9,1/8,1/7

0.034

DOS

7,8,9

1/4,1/3,1/2

7,8,9

1,1,1

1,1,1

0.310

CLS

6,7,8

1,2,3

7,8,9

1,1,1

1,1,1

0.318



Table 15. Pair wise comparison matrix and relative weights w.r.t solid and water waste treatment Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

1,1,1

1,2,3

1,1,1

1,1,1

0.222

OMR

1,1,1

1,1,1

1,1,1

2,3,4

1,1,1

0.259

IRT

1/3,1/2,1

1,1,1

1,1,1

1,1,1

1,1,1

0.171

DOS

1,1,1

1/4,1/3,1/2

1,1,1

1,1,1

1,1,1

0.162

CLS

1,1,1

1,1,1

1,1,1

1,1,1

1,1,1

0.186

Table 16. Pair wise comparison matrix and relative weights w.r.t temperature and humidity Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

2,3,4

1,1,1

1,2,3

1,1,1

0.282

OMR

1/4,1/3,1/2

1,1,1

1,1,1

1,2,3

1,1,1

0.191

IRT

1,1,1

1,1,1

1,1,1

1,1,1

1,2,3

0.214

DOS

1/3,1/2,1

1/3,1/2,1

1,1,1

1,1,1

1,1,1

0.149

CLS

1,1,1

1,1,1

1/3,1/2,1

1,1,1

1,1,1

0.164

IGI GLOBAL PROOF

Table 17. Pair wise comparison matrix and relative weights w.r.t natural hazards Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

1,1,1

1,2,3

1,1,1

2,3,4

0.282

OMR

1,1,1

1,1,1

1,1,1

1,2,3

1,1,1

0.214

IRT

1/3,1/2,1

1,1,1

1,1,1

1,1,1

1,2,3

0.199

DOS

1,1,1

1/3,1/2,1

1,1,1

1,1,1

1,1,1

0.164

CLS

1/4,1/3,1/2

1,1,1

1/3,1/2,1

1,1,1

1,1,1

0.141

Table 18. Pair wise comparison matrix and relative weights w.r.t inbound and outbound transport Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

3,4,5

6,7,8

2,3,4

1,2,3

0.348

OMR

1/5,1/4,1/3

1,1,1

1/4,1/3,1/2

1/7,1/6,1/5

1/9,1/8,1/7

0.038

IRT

1/8,1/7,1/6

2,3,4

1,1,1

1/3,1/2,1

1/5,1/4,1/3

0.102

DOS

1/4,1/3,1/2

5,6,7

1,2,3

1,1,1

1/3,1/2,1

0.200

CLS

1/3,1/2,1

7,8,9

3,4,5

1,2,3

1,1,1

0.311



Table 19. Pair wise comparison matrix and relative weights w.r.t alternative transport way Locations

RST

OMR

IRT

DOS

CLS

Relative Weights

RST

1,1,1

2,3,4

9,9,9

6,7,8

7,8,9

0.474

OMR

1/4,1/3,1/2

1,1,1

6,7,8

2,3,4

5,6,7

0.294

IRT

1/9,1/9,1/9

1/8,1/7,1/6

1,1,1

1/5,1/4,1/3

1/3,1/2,1

0.036

DOS

1/8,1/7,1/6

1/4,1/3,1/2

3,4,5

1,1,1

1,2,3

0.128

CLS

1/9,1/8,1/7

1/7,1/6,1/5

1,2,3

1/3,1/2,1

1,1,1

0.068

Table 21. Overall synthesized weighted scores for concrete production facility location Criteria

Sub-Criteria

Factors

Local Weights

SOF

0.340

ECF

0.285

SC

Local Weights

Global Weights

RST

LRC

0.742

0.25228

0.453

SPC

0.258

0.08772

IC

0.148

0.04218

TC

0.710

LC

CWP

OMR

IRT

DOS

CLS

0.049

0.281

0.139

0.078

0.341

0.058

0.297

0.205

0.099

0.109

0.182

0.038

0.423

0.248

0.20235

0.343

0.104

0.047

0.43

0.076

0.142

0.04047

0.375

0.031

0.164

0.293

0.136

0.742

0.173628

0.411

0.026

0.158

0.149

0.256

PD

0.258

0.060372

0.06

0.278

0.034

0.31

0.318

SWT

0.703

0.075221

0.222

0.259

0.171

0.162

0.186

TH

0.191

0.020437

0.282

0.191

0.214

0.149

0.164

NH

0.106

0.011342

0.282

0.214

0.199

0.164

0.141

IOT

0.100

0.0034

0.348

0.038

0.102

0.2

0.311

ATW

0.628

0.021352

0.474

0.294

0.036

0.128

0.068

RAS

0.272

0.009248

0.427

0.028

0.168

0.257

0.12

Relative Weights

IGI GLOBAL PROOF

TEF

0.234

ENF

0.107

TRF

Alternatives

0.034

Positive and negative ideal solutions are determined by taking the maximum and minimum values for each criterion using Eqs. (21) and (22). Then the distance of each alternative from PIS and NIS with respect to each subcriterion are calculated with the help of Eqs. (23) and (24). Table 22 shows the separation measure of each alternative form PIS and NIS. The closeness coefficient of each location alternatives is calculated by using Eqs. (25) and the ranking of the alternatives are determined according to these values. The results and final ranking are shown in Table 23.

From Table 23, it is evident that alternative DOS demonstrates highest score, hence, must be selected as an optimal location for concrete production facility. The order of ranking the alternatives using TOPSIS method results as follows: DOS > OMR > CLS > RST > IRT According to the final scores, it can be concluded that DOS can be the optimal location whereas IRT demonstrates the least possible location alternatives from the expert perception.



Table 22. Weighted normalized decision matrix for TOPSIS analysis Criteria

Alternatives

A*

A’

0.01968

0.01236

0.11428

0.00868

0.02991

0.00509

0.01784

0.01046

0.01784

0.00160

0.08701

0.01538

0.08701

0.00951

0.00664

0.01186

0.00550

0.01518

0.00125

0.00451

0.02743

0.02587

0.04445

0.00451

0.07136

0.01678

0.00205

0.01872

0.01920

0.00205

0.01920

0.01670

0.01948

0.01286

0.01219

0.01399

0.01219

0.01948

TH

0.00576

0.00390

0.00437

0.00305

0.00335

0.00305

0.00576

NH

0.00320

0.00243

0.00226

0.00186

0.00160

0.0016

0.00320

IOT

0.00118

0.00013

0.00035

0.00068

0.00106

0.00118

0.00013

ATW

0.01012

0.00628

0.00077

0.00273

0.00145

0.01012

0.00077

LRC

0.00395

0.00026

0.00155

0.00238

0.00111

0.00395

0.00026

RST

OMR

IRT

DOS

CLS

LRC

0.11428

0.01236

0.07089

0.03507

SPC

0.02991

0.00509

0.02605

0.01798

IC

0.00460

0.00768

0.0016

TC

0.06941

0.02104

0.00951

LC

0.01518

0.00125

CWP

0.07136

PD

0.00362

SWT

Table 23. Final evaluation of the location alternatives using TOPSIS analysis

IGI GLOBAL PROOF di+

di_

C i*

Rank

RST

0.1240

0.0690

0.358

4

OMR

0.0746

0.1227

0.622

2

IRT

0.1020

0.0680

0.400

5

DOS

0.0382

0.1223

0.762

1

CLS

0.0881

0.0993

0.530

3

Alternatives

3.2. Sensitivity Analysis A sensitivity analysis is realized to analyze the two steps fuzzy- AHP and TOPSIS methodology proposed in this study. For this reason, the weights obtained from fuzzy-AHP are changed for two criteria or factors while the others are constant. In other words, the weights of the first criteria SOF is changed with ECF, TEF, ENF and TRF, sequentially, while the others are constant. Then TOPSIS is operated to see the new results. Thus, the proposed methodology’s behavior against weight changes is observed in detail for discussion. Five mutual weight

changes are realized during the sensitivity analysis. More different weight exchanges can be applied to expand the sensitivity analysis. Thus the methodology result changes can be seen, and this helps the user determining priorities and making easier the evaluation process. The results of the sensitivity analysis can be seen from Table 24 and graphically from Figure 3. While the weights are changing mutually, the values of C* and the rankings are changing, too. If SOF’ and TRF’s weights are exchanged, then the C* value of RST springs from 0.358 to 0.426. And the ranking of RST is changes from 5 to 2. It is the most striking result of the



Table 24. Sensitivity analysis results using TOPSIS Sensitivity Analysis Number

1

2

3

4

5

Criteria

Alternative Locations

Factors

Local Weights

SOF

0.340

ECF

0.285

TEF

0.234

ENF

0.107

TRF

0.034

SOF

0.285

ECF

0.340

TEF

0.234

ENF

0.107

TRF

0.034

SOF

0.234

ECF

0.285

TEF

0.340

ENF

0.107

TRF

0.034

RST

OMR

IRT

DOS

CLS

0.358 (5)

0.622 (2)

0.4 (4)

0.762 (1)

0.530 (3)

0.351 (4)

0.546 (2)

0.333 (5)

0.723 (1)

0.445 (3)

0.406 (5)

0.626 (2)

0.448 (4)

0.764 (1)

0.499 (3)

C* (Rank)

IGI GLOBAL PROOF SOF

0.107

ECF

0.285

TEF

0.234

ENF

0.340

TRF

0.034

SOF

0.034

ECF

0.285

TEF

0.234

ENF

0.107

TRF

0.340

0.462 (3)

0.538 (2)

0.424 (4)

0.678 (1)

0.405 (5)

0.426 (2)

0.353 (3)

0.344 (4)

0.457 (1)

0.192 (5)

sensitivity analysis. IRT usually maintain forth place and has the lowest ranking, when the weights of SOF and ECF are exchanged. OMR has second or third ranking levels usually. CLS has its minimum ranking by the exchange of the weights of SOF with ENF and SOF with TRF. According to the sensitivity analysis results, DOS is determined to be the most appropriate alternative in all analyses (from 1 to 5). Because it has always maximum C* value after the weight exchanges carried out here.

4. DISCUSSION AND CONCLUSION In this paper, the evaluation of optimal concrete production facility location problem is handled. A two step fuzzy-AHP and TOPSIS methodology is structured here and TOPSIS uses fuzzy-AHP result weights as input weights. Then a numerical example is presented to show applicability and performance of the methodology. Also, a sensitivity analysis is hold to discuss and explain the methodology results.



Cost minimization has been the only objective of the existing literature in concrete production facility location selection and evaluation using different types of optimization techniques. In this study optimal concrete production facility location selection is evaluated by considering social, economical, technological, environmental, and transportation factors, thus making the decision more realistic and effectual. It can be said that using linguistic variables makes the evaluation process more realistic. Because evaluation is not an exact process and has fuzziness in its body. Here, the usage of fuzzy-AHP weights in TOPSIS makes the application more realistic and reliable. This multi-criteria hierarchical decision making model can be used in both public and private sector for concrete production facility location evaluation. As a future direction, other decision-making methods can be included in the methodology to ensure more integrated and/or comparative study. Also, heuristics can be implicated, too. As another direction, concrete production facility location evaluation criteria number can be increased. And a user friendly interface can be prepared to speed up and simplify the calculations. For further research, the results of our study can be compared with that of other fuzzy multi-criteria techniques like fuzzy ELECTRE, fuzzy PROMETHEE, or fuzzy VIKOR.

Chang, C. W., Wu, C. R., & Chen, H. C. (2008). Using expert technology to select unstable slicing machine to control wafer slicing quality via fuzzy AHP. Expert Systems with Applications, 34(3), 2210–2220. doi:10.1016/j.eswa.2007.02.042 Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. doi:10.1016/0377-2217(95)00300-2 Chowdhurya, R., Apul, D., & Fry, T. (2010). A life cycle based environmental impacts assessment of construction materials used in road construction. Resources, Conservation and Recycling, 54(4), 250–255. doi:10.1016/j.resconrec.2009.08.007 Csutora, R., & Buckley, J. J. (2001). Fuzzy hierarchical analysis: The Lambda-Max Method. Fuzzy Sets and Systems, 120, 181–195. doi:10.1016/S01650114(99)00155-4 Ertuğrul, Đ., & Karakasoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702–715. doi:10.1016/j.eswa.2007.10.014 Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 36(2), 4067–4074. doi:10.1016/j. eswa.2008.03.013

IGI GLOBAL PROOF

REFERENCES Boender, C. G. E., de Graan, J. G., & Lootsma, F. A. (1989). Multi-criteria decision analysis with fuzzy pairwise comparisons. Fuzzy Sets and Systems, 29(2), 133–143. doi:10.1016/0165-0114(89)90187-5 Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. doi:10.1016/0165-0114(85)90090-9 Cazacliu, B., & Ventura, A. (2010). Technical and environmental effects of concrete production: Dry batch versus central mixed plant. Journal of Cleaner Production, 18(13), 1320–1327. doi:10.1016/j. jclepro.2010.04.022

Hsu, P. F., Wu, C. R., & Li, Y. T. (2008). Selection of infectious medical waste disposal firms by using the analytic hierarchy process and sensitivity analysis. Waste Management (New York, N.Y.), 28(8), 1386–1394. doi:10.1016/j.wasman.2007.05.016 Hwang, C. L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin, Germany: Springer-Verlag. Joshi, R., Banwet, D. K., & Shankar, R. (2011). A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain. Expert Systems with Applications, 38(8), 10170–10182. doi:10.1016/j.eswa.2011.02.072 Kabir, G., & Hasin, M. A. A. (2011). Evaluation of customer oriented success factors in mobile commerce using fuzzy AHP. Journal of Industrial Engineering and Management, 4(2), 361–386. doi:10.3926/jiem.2011.v4n2.p361-386



Kabir, G., & Hasin, M. A. A. (2012). Multiple criteria inventory classification using fuzzy analytic hierarchy process. International Journal of Industrial Engineering Computations, 3(2), 123–132. doi:10.5267/j.ijiec.2011.09.007

Rajendran, S., & Gambatese, J. A. (2007). Solid waste generation in asphalt and reinforced concrete roadway life cycles. Journal of Infrastructure Systems, 13(2), 88–96. doi:10.1061/(ASCE)10760342(2007)13:2(88)

Kabir, G., & Hasin, M. A. A. (in press). Multicriteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network. International Journal of Industrial and System Engineering.

Rajesh, R., Pugazhendhi, S., & Ganesh, K. (2009). An analytic model for selection of and allocation among third party logistics service providers. International Journal of Enterprise Network Management, 3(3), 268–288. doi:10.1504/IJENM.2009.032398

Kutlu, A. C., & Ekmekçioglu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61–67. doi:10.1016/j. eswa.2011.06.044

Rajesh, R., Pugazhendhi, S., & Ganesh, K. (2011). Towards taxonomy architecture of knowledge management for third-party logistics service provider. Benchmarking: An International Journal, 18(1), 42–68. doi:10.1108/14635771111109814

Liang, G. S., & Wang, M. J. (1994). Personnel selection using fuzzy MCDM algorithm. European Journal of Operational Research, 78(1), 22–33. doi:10.1016/0377-2217(94)90119-8

Saleeshya, P. G., & Babu, A. S. (2011). Application of goal programming to manage agility in manufacturing systems. International Journal of Agile Systems and Management, 4(3), 222–237. doi:10.1504/ IJASM.2011.040516

Lin, M. C., Wang, C. C., Chen, M. S., & Chang, C. A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17–31. doi:10.1016/j. compind.2007.05.013

Saleeshya, P. G., Babu, A. S., and Vishnu, A. S. (2011). A model to assess the agility of manufacturing organisations: Systems approach and application. International journal of Productivity and Quality Management, 8(3), 265-295.

IGI GLOBAL PROOF

Liou, T. S., & Wang, M. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247–255. doi:10.1016/0165-0114(92)90223Q

Lippiatt, B., & Ahmad, S. (2004). Measuring the lifecycle environmental and economic performance of concrete: The BEES approach. In Proceedings of the International Workshop on Sustainable Development and Concrete Technology, Ames, IA (pp. 213-230). Mikhailov, L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, 134(3), 365–385. doi:10.1016/S01650114(02)00383-4 Murry, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. Review of Higher Education, 18(4), 423–436. Parsaei, S., Keramati, M. A., Zorriassatine, F., & Feylizadeh, M. R. (2012). An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry. International Journal of Industrial Engineering Computations, 3(2), 123–132. doi:10.5267/j.ijiec.2011.08.002 Partovi, F. Y. (2006). An analytic model for locating facilities strategically. Omega, 34(1), 41–55. doi:10.1016/j.omega.2004.07.018

Sen, C. G., & Çinar, G. (2010). Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max–min approach. Expert Systems with Applications, 37(3), 2043–2053. doi:10.1016/j.eswa.2009.06.075 Taskin, G. A. (2008). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 36(2), 4067–4074. Turhan, S., Arıkan, I. H., Yücel, B., Varinlioglu, A., & Köse, A. (2010). Evaluation of the radiological safety aspects of utilization of Turkish coal combustion fly ash in concrete production. Fuel, 89(9), 2528–2535. doi:10.1016/j.fuel.2010.01.010 Vidal, L. A., Marle, F., & Bocquet, J. C. (2011). Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert Systems with Applications, 38(5), 5388–5405. doi:10.1016/j.eswa.2010.10.016 Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870–880. doi:10.1016/j. eswa.2006.07.003



Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decisionmaking. Computers & Mathematics with Applications (Oxford, England), 53(11), 1762–1772. doi:10.1016/j.camwa.2006.08.037 Wang, Y. M., Luo, Y., & Hua, Z. (2007). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186(2), 735–747. doi:10.1016/j.ejor.2007.01.050

Xu, R. (2000). Fuzzy least square priority method in the analytic hierarchy process. Fuzzy Sets and Systems, 112(3), 395–404. doi:10.1016/S01650114(97)00376-X Zapata, P., & Gambatese, J. A. (2005). Energy consumption of asphalt and reinforced concrete pavement materials and construction. Journal of Infrastructure Systems, 11(1), 9–20. doi:10.1061/ (ASCE)1076-0342(2005)11:1(9)

Wang, Y. M., Yang, J. B., & Xu, D. L. (2005). A two-stage logarithmic goal programming method for generating weights from interval comparison matrices. Fuzzy Sets and Systems, 152, 475–498. doi:10.1016/j.fss.2004.10.020

IGI GLOBAL PROOF


International Journal of Productivity Management and Assessment Technologies An official publication of the Information Resources Management Association

Mission

The mission of the International Journal of Productivity Management and Assessment Technologies (IJPMAT) is to develop, promote and disseminate knowledge in the areas of productivity, efficiency, and performance management. The measurement of organizational productivity, performance, and efficiency is an essential part of change and contributes to general welfare of organizations and, to a larger extent, societies. By measuring productivity and efficiency, it is possible to evaluate the performance of an organization by comparing it with benchmarks of international best practices. Thus, from a societal point of view, productivity management is of high importance and value. The international dimension of the journal is emphasized to overcome cultural and national barriers and meet the needs of accelerating technology and changes in the global economy. Novel and fundamental theories, algorithms, technologies, and applications support this mission.

Subscription Information

IJPMAT is published quarterly: January-March; April-June; July-September; October-December by IGI Global. Full subscription information may be found at www.igi-global.com/ijpmat. The journal is available in print and electronic formats. Institutions may also purchase a site license providing access to the full IGI Global journal collection featuring more than 100 topical journals in information/computer science and technology applied to business & public administration, engineering, education, medical & healthcare, and social science. For information visit www.igi-global.com/isj or contact IGI at [email protected].

Copyright

IGI GLOBAL PROOF

The International Journal of Productivity Management and Assessment Technologies (ISSN 2160-9837; eISSN 2160-9845). Copyright © 2012 IGI Global. All rights, including translation into other languages reserved by the publisher. No part of this journal may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including classroom teaching purposes. Product or company names used in this journal are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. The views expressed in this journal are those of the authors but not necessarily of IGI Global. Correspondence and questions: Editorial:

Debaprosanna Nandy, PhD Editor-in-Chief, IJPMAT A.C. College of Commerce, University of North Bengal D.B.C. Road, Jalpaiguri - 735 101, West Bengal, India Telephone No.: +91 96097 93616 Fax: +91 3561 227 447 E-mail: [email protected]

Subscriber Info:

IGI Global Customer Service 701 E Chocolate Avenue Hershey PA 17033-1240, USA Tel: 717/533-8845 x100 E-mail: [email protected]

The International Journal of Productivity Management and Assessment Technologies is currently listed or indexed in: Bacon’s Media Directory; INSPEC; MediaFinder; The Standard Periodical Directory; Ulrich’s Periodicals Directory

igi global proof

igi global proof

Suggest Documents

igi global proof

igi global proof

igi global proof

igi global proof

igi global proof

igi global proof

igi global proof - iPEAR

igi global proof

igi global proof

igi global proof

igi global proof

igi global proof - iPEAR

igi global proof

igi global proof

igi global proof

igi global proof

igi global proof

IGI Global

igi proof

IGI Global

collaborative design - IGI Global

Preface - IGI Global

Preface - IGI Global

Preface - IGI Global