Critical Success Factors for the Iron and Steel Industry in Turkey:
A Fuzzy DEMATEL Approach
O¨ zgu¨r Kabak1 •Fu¨sun U¨ lengin2• Bora C¸ ekyay3•S¸ule O¨ nsel3• O¨ zay O¨zaydın3
Received: 28 April 2015 / Revised: 30 June 2015 / Accepted: 20 July 2015 / Published online: 2 August 2015 Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2015
Abstract The attempt to improve the efficiency and competitiveness of an industrial sector is aided by the determination of critical success factors (CSFs) which focus efforts in those areas that really affect the whole industry, thereby conserving limited resources. In this paper, a three-stage methodology is proposed to find CSFs for an industrial sector. The methodology specifies the interrelations between factors that shape the global com-petitiveness of a country as a whole and those that shape the competitiveness of the particular industry in question. It integrates a Web-based survey, a Delphi-type workshop, and a fuzzy decision making trial and evaluation laboratory model to highlight those CSFs upon which policymakers should especially concentrate in order to increase the competitiveness of a given industry. This methodology is then applied to a case study, identifying the CSFs of the iron and steel industry in Turkey. The results show that the
burden of custom procedures, total tax rate, scope and impact of taxation, and solidity of banks are the CSFs for the competitiveness of the Turkish iron and steel industry.
Keywords Critical success factors Decision making trial and evaluation laboratory (DEMATEL) Fuzzy set theory Delphi method Iron and steel industry
1 Introduction
Increasing a country’s productivity and competitiveness requires industrial and regional development policies in the form of effective industrial strategies. In order to enhance the competitiveness of an industry, it is of great importance to determine the elements or factors that impact the industry. Subsequently, decision-makers can focus on the priority factors to improve the industry. However, it is difficult to improve all influencing factors simultaneously. A more feasible method is to focus simply on a set of the most urgent and important factors and to work on these in a stepwise manner [1]. For this purpose, the concept of critical success factor (CSF) is used in this study. By identifying and discussing the CSFs of an industry, the factors having the greatest impact on the whole system can be discovered. Then policymakers can pay more attention to these CSFs and work on them progressively to achieve great improvements in the efficiency of the entire industry. According to Leidecker and Bruno [2], ‘‘CSFs are those characteristics, conditions, or variables that, when properly sustained, maintained, or managed, can have a significant impact on the success of a firm competing in a particular industry.’’ Rockart [3] developed the CSF concept as a way of identifying general managers’ information needs and & O¨zgu¨r Kabak
kabak@itu.edu.tr Fu¨sun U¨ lengin fulengin@sabanciuniv.edu Bora C¸ ekyay bcekyay@dogus.edu.tr S¸ule O¨ nsel sonsel@dogus.edu.tr O¨ zay O¨zaydın oozaydin@dogus.edu.tr
1 Industrial Engineering Department, Istanbul Technical University, Macka, 34367 Istanbul, Turkey
2 School of Management, Sabancı University, Orta Mahalle, Tuzla, 34956 Istanbul, Turkey
3 Industrial Engineering Department, Dogus University, Acibadem Zeamet Sok, 34722 Istanbul, Turkey
defined CSFs as ‘‘those few critical areas where things must go right for the business to flourish.’’
As demonstrated in Ulengin et al. [4] and Kabak et al. [5], a strong link also exists between the competitiveness of a country and the competitiveness of its industries. The success of a specific industry in a country, in other words, depends strongly on the national competitiveness of that country [6]. Therefore, the development level of the insti-tutions, the infrastructure, the macroeconomic environ-ment, the financial sector, as well as the health care and education level offered in the country in which the industry operates greatly affect the competitiveness of this industry [1]. Consequently, the CSFs that play a primary role in the competitiveness of an industry are not solely under the control of the industry but also depend on the external factors that shape the competitiveness of the nation in which the industry is located.
This study proposes a methodology to identify which of the critical success factors shaping the competitiveness of a country significantly affect the competitiveness of an industry. The proposed methodology is based on the inte-gration of three main stages: a Web-based survey, a Del-phi-type workshop, and the fuzzy decision making trial and evaluation laboratory (DEMATEL) method. In the prob-lem-structuring phase, which constitutes the first two stages of the methodology, the most relevant factors that are very important for a specific industry, and the relations between these factors are determined. Fuzzy DEMATEL method is then used to rank the selected success factors upon which policymakers should immediately concentrate to increase the competitiveness of the industry.
The proposed methodology is applied to reveal the critical success factors for the Turkish iron and steel industry. In Turkey, the iron and steel industry is one of the crucially important industries that have high market share and have seen high annual average export growth rate over the years [7]. The iron and steel sector is also of great importance for the general performance of the manufac-turing industry in Turkey due to its increasing production capacity, export potential, and the inputs it provides to other sectors. The iron and steel industry has always played a vital role in the industrial and economic development of Turkey and offers a great potential for growth in the future [8].
To our knowledge, this is the first study that attempts to reveal which of the basic factors shaping the competitive-ness level of a nation will play an important role in improving the success of the iron and steel industry. The main contributions of the study can thus be listed as follows:
• A problem-structuring methodology to identify the relevant concepts as well as their inter-relationships,
• A three-stage methodology for analyzing the compet-itiveness of an industry, and
• An application of the proposed methodology to the iron and steel industry in Turkey.
The second section of the paper provides a literature survey, briefly summarizing the existing studies on com-petitiveness in the iron and steel industry as well as on the identification of critical success factors. The third section presents the framework of the proposed methodology. The fourth section provides an application of the proposed methodology to the Turkish iron and steel industry and highlights its CSFs. The last section draws conclusions and presents suggestions for further research.
2 Literature Review
2.1 Competitiveness in Iron and Steel Industry
The issue of how to raise competitiveness becomes critical for industries in a global market place. Even though industries differ in appearance, the problems they face are very similar. Therefore, a particular industry analysis can serve as a template in order to offer a useful guide for policymakers. Based on this template, it will be easier to build a reliable model from limited data which can specify the basic factors enhancing the competitiveness of the industry under investigation.
Assessing industrial competitiveness is a complex task. First of all, one needs measures of competitiveness to arrive at a comparable figure for each industry [1,4–6,9]. Unfortunately, there are few studies which assess the competitiveness of the iron and steel industry of any one given country.
The competitiveness of three large iron and steel man-ufacturing enterprises in China is the focus of Wu and Zhong [10]. The paper investigates the impact of using e-business resources on enterprise competitiveness by focusing on the profitability dimension. The results show that to increase enterprise competitiveness in the iron and steel industry, e-business resources are necessary but not sufficient.
Ohashi [11] analyses the impact of export subsidies on an industry’s cost competitiveness in the presence of learning by doing. This paper focuses on the Japanese steel industry in the 1950s and 1960s. Based on the simulations with the model, the research findings underline that subsidy policy had an insignificant impact on the growth of com-petitiveness of the industry because the estimated steel supply function was relatively inelastic.
The iron and steel sectors have significantly high CO2 emissions and relative openness to international trade when
compared to the other sectors covered by the European Emission Trading Scheme (ETS) [12]. Anger and Obern-dorfer [13] and a majority of the papers cited by them assess the impact of European ETS from the production dimension of competitiveness and generally conclude that this impact on European iron and steel production is rela-tively modest.
Smale et al. [14] give the results of another analysis that highlights the positive impact of European trade emissions on the profitability of iron and steel industry competitiveness.
Demailly and Quirion [15] analyze the impact of the European ETS on both production and profitability dimensions of competitiveness for the iron and steel industry using a partial equilibrium model. They show that competitiveness losses are small for this sector.
Singh et al. [16] underline that steel companies are becoming aware of sustainability challenges and present a method for the development of a composite sustainability index to specify the sustainable performance of steel industries along economic, environmental, and societal pillars of sustainability. They use the Analytical Hierarchy process to determine the weights at various levels, evaluate sub-indices, and aggregate them for a composite index. The effectiveness of the proposed method is illustrated by a case study for a major steel company in India.
The literature review presented above demonstrates that the indicators and drivers of competitiveness are multi-faceted in nature and possess complex inter-relationships. Therefore, a single aspect, or even just a few aspects, will not be sufficient to thoroughly explain competitiveness at the industrial level. Competitiveness has economic, social, and environmental dimensions. In order to increase the competitiveness level of each industry, the basic critical success factors should be highlighted, which becomes possible when a broad alliance of governments and private sectors are working together.
2.2 Identifying Critical Success Factors
CSFs are the areas in which satisfactory results will guar-antee successful competitive performance for an organi-zation. There are generally a large number of factors that are effective on success. The key to success is, thus, to focus the most limited resource (time) on those areas that really make the difference between success and failure [17]. Identifying CSFs is therefore vital for the efficient use of resources in achieving the desired results in an organi-zation. However, CSFs are complex in nature and neces-sitate the consideration of a high number of parameters in order for the full picture to emerge [18].
The CSF approach has been widely adopted and used in different fields. Much of the related literature presents a
diverse range of studies that identify CSFs in various domains. Belassi and Tukel [19] concentrate on the critical factors that affect project success or failure. Chan et al. [20] develop a conceptual framework on CSFs specifically affecting the performance of construction projects. Karlsen et al. [21] study CSFs in information technology projects. The CSFs in customer relationship management (CRM) applications are analyzed by King and Burgess [22]. Getz and Brown [23] attempt to identify the CSFs for a tourism facility to attract wine consumers. The CSFs for enterprise resource planning (ERP) implementations are investigated by Hong and Kim [24]. Some other research areas where there is a need for identifying CSFs are knowledge man-agement, integrating suppliers into new product develop-ment, business process managedevelop-ment, and humanitarian aid supply chains [25–28].
Addressing problems in the field of decision making, DEMATEL method meets the objective of understanding the causal relationships between elements. It is used to develop a structural model of a system using expert knowledge [29]. DEMATEL’s basis in graph theory enables it to visually analyze and solve problems. In this structural modeling approach, the interdependent relation-ships between the factors influencing the system under consideration can be represented in the form of a directed graph, called a cause-effect diagram. In this way, the fac-tors can be divided into cause and effect groups by which a better understanding of causal relationships can be obtained [30]. This feature makes DEMATEL an appro-priate tool to solve complex system problems [31–34]. It not only provides a way to visualize causal relationships between criteria through an impact-relationship map but also indicates the degree to which criteria influence each other [35,36].
DEMATEL is also used for identifying CSFs in a number studies. Wu [37] uses fuzzy DEMATEL method to segment the critical factors for successful knowledge management implementations. Sun [18] proposes DEMA-TEL as the main analytical tool to handle the inner dependences within a set of criteria used in Porter’s Dia-mond model and to find the CSFs for the electronic design automation industry. The author concludes that the critical local demand condition and government are the causal competitive advantage factors which could play a signifi-cant role in shaping this industry. Sun [18] also provides a useful literature survey on the application of DEMATEL in different research areas. Wu and Chang [38] use fuzzy DEMATEL method to identify critical factors and their causal relationships in the electronic sector in green supply chain management. Govindon et al. [39] developed green supply chain management practices and performances for the automotive industry and investigate the importance and causal relationships between them using a fuzzy
DEMATEL approach. Jeng [40] applies fuzzy DEMATEL technique to explore the causal relationships between key dimensions in supply chain collaboration to enable better strategic development of manufacturing firms in Taiwan. Sangari et al. [41] developed a hybrid evaluation method that integrates fuzzy logic, DEMATEL, and Analytic Network Process to identify critical factors for achieving supply chain agility.
This paper will use the DEMATEL method to classify the factors influencing an industry and to identify the CSFs related to the success of that industry. Because DEMATEL provides information on the impact of each factor on the whole system it is possible to determine the most influential factors on the system by analyzing and interpreting the structural model. The causal factors having the greatest effect on the system are obviously CSFs [30]. The only remaining problem is that, in order to implement the DEMATEL approach to identify CSFs, the degree of direct effect between each pair of factors is needed. These degree scores are always acquired through expert surveys. In this study, a Delphi workshop is conducted in order to obtain these degrees.
In decision making, assuming the judgments of experts to be exact values (equating to crisp scores) may be inappro-priate and not reflect the vagueness of the real world. Fuzzy logic is, paradoxically enough, a precise logic of imprecision and approximate reasoning [42]. Zadeh [42] points out two human capabilities that fuzzy logic formalizes: first, the capability to converse, reason, and make rational decisions in an environment of imperfect information and second, the capability to perform a wide variety of physical and mental tasks without measurements and computations. Therefore, fuzzy logic-based fuzzy set theory is an important tool to model those types of uncertainties that result from vague and imprecise linguistic expressions.
To benefit from advantages of both DEMATEL and fuzzy theory, several methods incorporating them have been proposed in the literature [for example see [43] and [44] ]. These methods enable researchers to make better decisions in an environment of imperfect information characterized by experts’ linguistic expressions. In partic-ular, when experts make judgments using incomplete or conflicting information, or when they are aware of the partiality of truth or lack expertise in some situations, the contributions of these methods will increase. Due to these advantages, the fuzzy DEMATEL method is chosen to identify CSFs in this study.
3 Proposed Methodology
The objective of the model built in this study is to identify CSFs and to explore, through an extensive analytical model, the influences of the factors in the provision of a
national competitive advantage on the provision of com-petitive superiority in the sector of particular interest for the study.
Within the fundamental framework of the model pre-sented in Fig.1, the problem is structured in two stages. The first stage is to list those concepts related to the competitiveness of the country which are likely to influ-ence the sector. In the second stage, the relations between these concepts are determined. In fact, these two stages are similar to methodology proposed in Ulengin et al. [4] and Kabak et al. [5].
The final stage is to determine the CSFs influencing the sector with regard to overall long-term relations between the concepts. The details of the proposed stages are given in Fig. 1.
3.1 Listing the Concepts
In accordance with the proposed methodology, it is sug-gested here that a list of concepts is prepared to determine which of the factors in country competitiveness are most closely related to the sector in question. The concept list is formed by determination of related components among the 111 components defined in the WEF report [1]. Both the-oretical and empirical evidence reveal an abundance of critical constituents that comprise the global competitive-ness of a country. The Global Competitivecompetitive-ness Index (GCI) developed by the World Economic Forum (WEF) suggests that the sorting of countries by competitiveness should take into consideration many parameters such as labor market efficiency, technological readiness, and financial market development [1]. The WEF ranks 144 countries based on their scores on 111 concepts in 12 pillars. Many countries recognize the GCI as a measure for correctly defining competitiveness and measuring a country’s competitive strength. The rankings provided by GCI serve as bench-marks for policymakers and other interested parties to judge the competitive success of their countries within a global context. This study can therefore be useful in identifying the additional significance of the factors used by WEF in assessing the effects of competitive power of a
Listing the concepts -WEB based
survey-Determining the relations between concept -Delphi type
workshop-Identifying critical success factors -Fuzzy DEMATEL-Stage 1 Stage 2 Stage 3 Pr o b le m St ruct u ri ng P h a se Mo d e lin g Ph a se
country on the industrial competitive power of a single sector within that country.
To this end, a Web-based survey is conducted, in which experts are asked to grade the relevancy of each of the 111 concepts in the WEF report to the industry under study. The grading is performed on a scale of 1 to 10 (1: the concept is not related to the sector at all; 10: the concept is very closely related to the sector). Afterwards, the average of the grades that these concepts receive from all the respondents is calculated, and a threshold is determined. Concepts that scored above this threshold form the concept list. Concepts not listed in the WEF report but thought to be important for the sector under study can also be added to the list in line with the experts’ opinions.
3.2 Determining the Relations between the Concepts by a Delphi Workshop
The purpose of the second stage is to create a relational network between the concepts that participants consider to be very important in shaping the future of the sector under study. In this way, it becomes possible to determine the impact of each concept on the others and to use it as an input to the fuzzy DEMATEL model. To identify the expert opinions on the interrelations between the concepts, a workshop is organized. This workshop uses the Delphi approach, which is commonly used in group decision-making applications. The Delphi method consists of the following four phases [45]:
1. Exploration of the subject under consideration, 2. Understanding group views,
3. Exploration and discussion of the different points of view, and
4. Final evaluation.
These general phases can be used for a variety of pur-poses, including evaluating possible budget allocations, exploring urban and regional planning options, and revealing personal value priorities. In the context of the critical success factor determination problem, this general method can be used as follows.
In the first phase, the purpose of the study and the steps of the process which will be followed are presented to the participants. The list of concepts and a detailed description of each concept are also provided to the participants. Moreover, each participant is allowed to contribute any information that he/she considers to be relevant to the issue in this phase.
In the second phase, the participants are divided into groups, preferably with an odd number of members (it is also important that the members of each group have similar social and occupational status). Then the groups discuss the direct relations among the concepts in a pairwise manner.
The linguistic expressions given in the second column of Table1 are used to evaluate the impact of the relation between any two concepts. The objective of this activity is to reveal the hidden information through discussions within each group, to clearly define the concepts so they are uniformly understandable by all participants, and finally to state the relations between concepts.
In the third phase, the evaluations of the different groups are combined, summarized, and shared with the other groups. The results of the groups can be summarized in terms of first quadrant, second quadrant, median, average, and range for every concept in the survey. This step enables the results from different groups to be discussed within the other groups and to create a common frame of mind.
The fourth stage may involve disagreements between the groups about the impact level of the concepts, where each group presents arguments and counter-arguments, and an open discussion takes place to reach a compromise decision. The groups may or may not change their previous assess-ments after the discussions. In case of a change in the pre-vious assessments, the scores of the groups are recombined, and all the participants discuss the outcomes again with contributions. This process can be repeated until the com-promise impact scores for each pair of concepts are obtained. Once these impact scores have been obtained from the workshop, they are used as input to the fuzzy DEMATEL method, which identifies the CSFs for the competitiveness of the industry under study.
3.3 Exploration of the CSFs Via Fuzzy DEMATEL
In the previous section, the direct relations between the concepts were determined. In this stage, those direct rela-tions are used to determine overall long-term relarela-tions, including indirect relations. To begin with, some basic information related to the fuzzy logic is provided, and this is followed by a description of the fuzzy DEMATEL procedure used in this study.
3.3.1 Fuzzy Set Theory
Fuzzy set theory is an important tool to model the uncer-tainties that result from vague and imprecise linguistic Table 1 Linguistic expressions used in the workshop
Scale levels Linguistic expression Triangular fuzzy numbers 3 Strong relation (2/3, 1, 1)
2 Moderate relation (1/3, 2/3, 1) 1 Weak relation (0, 1/3, 2/3) 0 No relation (0, 0, 1/3)
expressions. It is capable of reflecting the ambiguity of the human judgments required in the proposed model to specify interrelations between the factors. In order to deal with the ambiguities involved in the process of linguistic estimation, linguistic terms are converted to fuzzy num-bers. Definitions required for the presentation of proposed fuzzy DEMATEL approach are as follows:
Definition 1 [43]. A fuzzy set ~A is a subset of universe of discourse X, which is a set of ordered pairs and is char-acterized by membership function lA~ðxÞ representing a mapping lA~: X! ½0; 1. lA~ð Þ 2 ½0; 1, where lx A~ð Þ ¼ 1x indicates that x completely belongs to the fuzzy set ~A and lA~ð Þ ¼ 0 reveals that x does not belong to ~x A.
Definition 2 [43]. A fuzzy set ~A of the universe of dis-course X is convex if
lA~ðkx1þ 1 kð Þx2Þ min l~Að Þ; lx1 A~ð Þx2
8x 2 x1; x2½ ; where k2 0; 1½ :
Definition 3 [43]. A fuzzy set ~A of the universe of dis-course X is normal if
maxlA~ð Þ ¼ 1:x
Definition 4 [43]. A fuzzy number ~N is a fuzzy subset in the universe of discourse X, which is both convex and normal.
Definition 5 [43]. A triangular fuzzy number ~N is a tri-plet (l, m, r), where the membership function l~Nð Þ isx defined as follows: lN~ð Þ ¼x ðx lÞ=ðm lÞ l x m ðr xÞ=ðr mÞ m\x r 0 otherwise 8 < : ;
where l, m, and r are real numbers and l B m B r.
3.3.2 Fuzzy DEMATEL Approach
In general, the fuzzy DEMATEL procedure proposed in Wu and Lee [43] is used in this paper, except for two modifications required for its use in the proposed methodology. First, the initial step of the procedure in Wu and Lee [43] is skipped because this step was performed in Stage 1 and Stage 2 of the proposed methodology. In addition, when aggregating the assessments of the deci-sion-makers, if the individual assessments are defuzzified before aggregation then some important information regarding the distribution of the individual assessments may be lost. Therefore, in this study, individual assess-ments are first aggregated as fuzzy numbers, and then the Converting Fuzzy data into Crisp Scores (CFCS) procedure is applied for defuzzification.
Step 1. Designing the fuzzy linguistic scale for group evaluation Suppose that P groups evaluated N variables in a pairwise manner and that their evaluations are repre-sented as triangular fuzzy numbers ~epij¼ lpij; m
p ij; r p ij ; p¼ 1; . . .; P; i; j¼ 1; . . .; N according to the linguistic expres-sions provided by the groups and the related triangular fuzzy numbers defined in Table 1. Triangular fuzzy num-bers related to linguistic expressions are defined based on linguistic hierarchical structure explained in Herrera and Martı´nez [46].
Step 2. Aggregating the assessments of the decision-makers In this step, the evaluations of the groups are first aggregated into a single fuzzy number as follows: ~
eij¼ lij; mij; rij ffi Aggregate ð~epij; p¼ 1; . . .; PÞ; ð1Þ where lij¼ min p¼1;::;Pl p ij; ð2Þ mij¼ X p¼1;::;P mpij=P; ð3Þ rij¼ max p¼1;::;Pr p ij: ð4Þ
The other equations are the same as those used in the CFCS method proposed by Opricovic and Tzeng [47]:
xlij¼ ðlij minlijÞ=Dmaxmin; ð5Þ
xmij¼ ðmij minlijÞ=Dmaxmin; ð6Þ
xrij¼ ðrij minlijÞ=Dmax
min; ð7Þ
xlsij¼ xmij=ð1 þ xmij xlijÞ; ð8Þ
xrsij¼ xrij=ð1 þ xrij xmijÞ; ð9Þ
xij¼ xlsij 1 xlsij þ xrsij xrsij = 1 xlsijþ xrsij; ð10Þ
zij¼ min lijþ xijDmaxmin; ð11Þ
where Dmaxmin ¼ max rij min lij and zij is a crisp value representing the direct effect of variable i on variable j. As a result, the initial direct-relation matrix Z = [zij]N9N is obtained.
Step 3: Establishing and analyzing the structural model Using the initial direct-relation matrix Z, the normalized direct-relation matrix X and the total-relation matrix T can be obtained as follows: X¼ s Z; ð12Þ where s¼ 1 max1 i NPNj¼1zij ; i; j¼ 1; 2; . . .; N ð13Þ and
T¼ X 1 Xð Þ1: ð14Þ A causal diagram can be generated using the following equations: T¼ tij NN; ð15Þ Di¼ XN j¼1 tij; ð16Þ Rj¼X N i¼1 tij; ð17Þ
where D and R denote the sum of rows and the sum of columns, respectively. The causal diagram is constructed with the horizontal axis (D ? R) named ‘‘Prominence’’ and the vertical axis (D - R) named ‘‘Relation.’. (D ? R) pre-sents the index of the degree of influences given and received. Therefore, (D ? R) reveals the strength of the central role that factors play in the problem and the hori-zontal axis ‘‘Prominence’’ shows how much importance the factor has. On the other hand, (D - R) shows the difference between a single factor’s influence on other factors and the influence of others on that factor.
Factors having positive (D - R) value are less affected by the other factors than they, in turn, have impact on those factors. Factors having negative (D - R) value are more affected by the others. Therefore, the vertical axis ‘‘Rela-tion’’ divides the factors into cause and effect groups, where the factors having high positive (D - R) are in the cause group. If any improvement can be realized in the cause group factors then it will result in a subsequent improvement in the other factors. Therefore, if high-level performance is intended for all the system factors, priority should be devoted to cause group factors. In short, the variables having high (D - R) values compared to others can be considered as the CSFs [7, 35, 37, 48–50]. For further explanations and examples of using (D - R) value to identify CSFs, please see [30,37,51,52].
4 A Case Study in Turkey
The methodology developed in this paper has been applied to the problem of determining the CSFs in sustaining and improving the competitiveness of the Turkish iron and steel industry. The primary reason behind this selection is that the iron and steel industry in Turkey is of great importance for the general performance of the manufacturing sector in Turkey because of its increasing production capacity, export potential, and inputs to other sectors [53]. It is also one of the most important industries in Turkey, with a target of 55 billion dollars in Turkey’s projected 500 billion dollar export target to be achieved by 2023 [54].
Because of China’s new investments which have boos-ted its share of world iron and steel production in recent years and the horizontal-vertical consolidations taking place worldwide, Turkey should take special measures to maintain its place in the iron and steel sector. In this context, it is essential to develop long-term competition strategies using a model which will not only determine the factors needed to secure competitive superiority but which will also look into the causes of the losses in the iron and steel sector. Details of how the proposed methodology has been applied to the Turkish iron and steel industry are presented in the following sections.
4.1 Listing the Concepts
In the framework of the first stage, a Web-based survey was conducted in April 2011 to determine the components of the model. The participants were asked to grade, on a scale of one to ten, the relevance of the 111 WEF indicators to the success of the Turkish iron and steel industry. In total, 36 people took part in the survey. The participants were from a wide spectrum of the economy, including the private sector, non-governmental organizations, and the public sector. All concepts were then listed according to their average grades; concepts with an average grade of at least 8.5 were chosen to be used in the model, meaning that a consensus was reached on the list of the concepts. This cut-off point (i.e., 8.5), specified as the break point of average grades for the concepts used, was decided upon by a consensus of the top executives from the Turkish Fed-eration of Industrial Associations (Sekto¨rel Dernekler Federasyonu—SEDEFED) and top managers of the Turk-ish Steel Producers Association (Tu¨rkiye C¸ elik U¨ reticileri Derneg˘i—TC¸ U¨ D).
In line with the results of the Web-based survey, the concepts that were agreed upon as influencing the future of the Turkish iron and steel industry are listed in Table2 (ID = 1–24). Following an e-meeting with the sector’s main stakeholders to assess the survey results, three addi-tional concepts, which can be used as proxy variables for the competitive power of the iron and steel industry, were added to the list (ID = 25–27).
4.2 Determining the Relations Between the Concepts
The second stage in constructing the model is to create a relational network between those concepts which partici-pants believe to be very important in shaping the future of the sector under study. In this way, it is possible to deter-mine how large an impact the concepts have on one another. To this end, a Delphi workshop with participants from a wide range of economic sectors was organized. The
participants consisted of six academicians, 13 representa-tives from industry, six representarepresenta-tives from federations and associations, and one representative from the Ministry of Science, Industry, and Technology. The workshop took a full day and consisted of four main phases.
At the beginning of the workshop, in the first phase, participants were briefly informed about the modeling project. The moderator gave a presentation to explain the aim of the study, the definitions of the indicators, and the process to be followed during the workshop.
In the second phase, participants were divided into six homogeneous groups and asked to determine whether a relation existed between the concepts given in Table2. Participants were also asked to determine the degree of the relation, if any, using the linguistic expressions in Table1. In this phase, because the number of variables was quite large, three groups were asked to evaluate the first half of the list and the other three groups the second half.
In the third phase of the workshop, the results of the previous phase obtained from each group and the aggregate
results (given as first quartile, third quartile, median, and range) were prepared and given out as a priori information for the next evaluation. Then the groups reconsidered their evaluations.
In the fourth stage of the workshop, the results of the previous phase were recalculated and presented to all participants. The participants discussed any disagreements among the group evaluations. Groups were free to revise their evaluations for a final time. Detailed explanations were provided for any issues that were not agreed upon to enable the participants to reach a consensus on the mean-ings of these issues and thereby to come to an agreement. Then, following a revote, the final agreed-upon ation was obtained. As an illustration, some of the evalu-ation results from the first group are given in Table 3(due to the large size of the table, only a small part is shown). According to the first group’s evaluations presented in Table3, for instance, Soundness of banks (ID#1) has a weak positive effect on Availability of scientists and engineers (ID#2).
4.3 Identifying Key Success Factors Using FUZZY DEMATEL
In this stage, the CSFs for the iron and steel industry were determined by combining the evaluations made by the three groups in the previous stage. To this end, the evalu-ations of the three groups, expressed as triangular fuzzy numbers, were first combined according to Eqs. (1–4).
For instance, the evaluations of the groups for (2,4) are ~ e124¼ 1 3; 2 3; 1 ; ~e224¼ 2 3; 1; 1 ; ~e324 ¼ ð2 3; 1; 1Þ; respectively. Therefore, the corresponding aggregate evaluation ~e24 ¼
l24; m24; r24
ð Þ is calculated using Eqs. (2–4) as follows: l24 ¼ minp¼1;::;3lpij¼ min 1 3; 2 3; 2 3 ¼1 3 based on Eq. (2), m24¼Pp¼1;::;3lpij=P¼ 2 3þ 1 þ 1 =3¼ 0:89 based on Eq. (3), r24¼ max p¼1;::;3r p
ij¼ max 1; 1; 1ð Þ ¼ 1 based on Eq. (4). Examples of calculated results are given in Table4. Then the initial direct-relation matrix Z, shown in Table5, is obtained using Eqs. (5–11).
In this application, minlij¼ 0, max rij¼ 1, and Dmaxmin ¼ max rij min lij¼ 1. Therefore,
xlij¼ ðlij minlijÞ=Dmaxmin ¼ ðlij 0Þ=1 ¼ lij; based on Eq. (5)
xmij¼ ðmij minlijÞ=Dmaxmin ¼ ðmij 0Þ=1 ¼ mij; based on Eq. (6)
xrij¼ ðrij minlijÞ=Dmaxmin ¼ ðrij 0Þ=1 ¼ rij; based on Eq. (7).
Table 2 List of concepts
ID Concepts
1 Soundness of banks
2 Availability of scientists and engineers 3 Quality of railroad infrastructure 4 Foreign market size index 5 Trade tariffs
6 Quality of electricity supply 7 Quality of overall infrastructure 8 Burden of customs procedures 9 Domestic market size index 10 Efficacy of corporate boards 11 Quality of port infrastructure 12 Extent of marketing 13 Extent of staff training
14 Reliance on professional management 15 Nature of competitive advantage 16 Availability of latest technologies 17 Firm-level technology absorption 18 Total tax rate
19 Production process sophistication 20 Extent and effect of taxation
21 Foreign direct investment and technology transfer 22 Intensity of local competition
23 Local supplier quality 24 Local supplier quantity
25 Iron and steel foreign market efficiency 26 Iron and steel domestic market size
Then, Eqs. (8–10) are used to find the xij values. For instance, x12is found as follows:
xls12 ¼ xm12=ð1 þ xm12 xl12Þ ¼ 0:44=ð1 þ 0:44 0Þ ¼ 0:306; based on Eq. (8) xrs12¼ xr12=ð1 þ xr12 xm12Þ ¼ 0:67= 1 þ 0:67 0:44ð Þ ¼ 0:545; based on Eq. (9) x12¼ xls12½ ð1 xls12Þ þ xrs12 xrs12= 1 xls12½ þ xrs12 ¼ 0:306 1 0:306½ ð Þ þ 0:545 0:545= 1 0:306 þ 0:545½ ¼ 0:412; based on Eq. (10).
Because minlij¼ 0 and max rij¼ 1, it is clear that zij¼ minlijþ xijDmaxmin ¼ xij based on Eq. 11. Therefore, z12 ¼ x12¼ 0:412, as shown in Table5.
Then, using Eqs. (12–14), X and T are determined. The total relation matrix T is given in Table6. Finally, the Di and Ri values are calculated according to Eqs. (15–17); Table7and Fig. 2show the results.
The values of (D ? R) show each factor’s degree of importance, and the values of (D - R) divide the factors into cause and effect groups. Because the objective is to discover those factors that have the greatest effect on the system, the factors that have high (D - R) values are considered as the CSFs for the iron and steel industry. Therefore, we conclude that Burden of customs procedures (ID = 8), Total tax rate (ID = 18), Extent and effect of taxation (ID = 20), and Soundness of banks (ID = 1) are the CSFs for the competitiveness of the iron and steel industry.
On the other hand, looking at (D ? R) values makes it possible to analyze the system’s most important concepts. These concepts for the iron and steel industry were found to be Iron-steel level of development of production process (ID = 27), Nature of competitive advantage (ID = 15), and Iron and steel foreign market efficiency (ID = 25).
4.4 Discussion of the Results
As a result of the Delphi-type workshop and the fuzzy DEMATEL analysis which were carried out to determine CSFs for the competitive improvement of the Turkish iron Table 3 Evaluations from the first group
ID 1 2 3 4 5 … 23 24 25 26 27 1 1 1 2 0 … 2 2 2 2 1 2 0 1 2 0 … 3 1 2 1 3 3 0 0 3 0 … 1 1 2 1 1 4 2 1 1 2 … 1 1 3 0 2 5 0 0 1 1 … 1 0 1 1 0 : : : : : : : : : : : 23 0 1 0 1 0 … 1 2 1 2 24 0 2 2 1 0 … 2 2 0 1 25 3 2 2 3 3 … 2 1 0 2 26 3 2 2 0 0 … 2 2 2 1 27 3 3 2 3 0 … 3 2 3 3
Table 4 Aggregate evaluations of the groups—~eij¼ lij; mij; rij
1 2 3 4 … 25 26 27 1 (0, 0, .33) (0, .44, .67) (0, .44, .67) (0, .56, 1) … (0, .67, 1) (0, .44, .67) (0, .56, 1) 2 (0, .22, .67) (0, 0, .33) (0, .33, .33) (.33, .89, 1) … (0, .44, .67) (0, .33, .67) (.33, .89, 1) 3 (0, 0, .33) (0, 0, .33) (0, 0, .33) (0, 0, 0) … (.33, .78, 1) (0, .44, .67) (0, .44, .67) : : :. : … … … : : : 25 (.33, .89, 1) (.33, .89, 1) (0, .67, 1) (0, .56, .67) … (0, 0, .33) (0, .44, .67) (.33, .78, 1) 26 (.33, .89, 1) (.33, .78, 1) (.33, .89, 1) (0, .44, .67) … (0, .44, .67) (0, 0, .33) (0, .11, .33) 27 (.67, 1, 1) (.67, 1, 1) (.33, .78, 1) (0, 0, 0) … (.67, 1, 1) (.67, 1, 1) (0, 0, .33)
and steel industry, the following success factors were identified as having the greatest influence on the industry: the soundness of banks, the extent and effect of taxation, the total tax rate, and the burden of customs procedures.
Turkey has to improve the health of its banks with sound balance sheets. In fact, before 2001, the major sources of financial instability in Turkey were macroeconomic insta-bility and government involvement. At present, Turkey is closer to achieving macroeconomic stability than ever before, and the government is reducing its direct involve-ment. Major strides have been made after the crisis of 2001 in cleaning up a very nontransparent and politicized banking environment and in upgrading the regulatory structure to EU standards. Further consolidation and mergers with foreign partners will be inevitable. According to the WEF report (2012), Turkey is in 22nd place out of 144 countries on the soundness of banks [1]. If EU inte-gration becomes a concrete vision for the future, macroe-conomic stability will be firmly rooted in Turkey, and the banking sector will quickly move to EU standards, long before any eventual accession date. This trend will, in its
turn, initiate further improvements in the competitiveness of the iron and steel industry.
Another policy implication for Turkey should be to increase the impact of level of taxes on incentives to work or invest. In fact, the tax rate variable is a combination of profit tax (% of profits), labor tax and contribution (% of profits), and other taxes (% of profits) [1]. As for the impact of taxation, according to the WEF report (2012), Turkey ranks 88th out of 144 countries in terms of total tax rate and 117th out of 144 countries in terms of the scope and impact of taxation [1]. The country’s ranking shows the importance of making improvements in the area of taxa-tion. The iron and steel industry in China, for example, has been stimulated by strong domestic demand, particularly from construction, manufacturing, and automotive indus-tries, leading to rapid growth in the iron and steel industry in recent years. In 2002, the government reduced the resource tax on iron ore by 40 % for those vertically integrated entities involved in both mining and metallur-gical processing. The tax reduction was in line with the government’s policy of promoting integrated iron and steel Table 5 Initial direct-relation
matrix (zij) 1 2 3 4 5 … 23 24 25 26 27 1 0.050 0.412 0.412 0.794 0.283 … 0.474 0.595 0.594 0.412 0.531 2 0.283 0.050 0.276 0.794 0.283 … 0.794 0.214 0.412 0.350 0.794 3 0.050 0.050 0.050 0.663 0.050 … 0.412 0.412 0.717 0.412 0.412 4 0.594 0.474 0.412 0.050 0.412 … 0.594 0.474 0.794 0.283 0.595 5 0.140 0.050 0.350 0.663 0.050 … 0.474 0.412 0.350 0.276 0.283 .: : : : : : : : : : : 23 0.214 0.474 0.140 0.412 0.050 … 0.050 0.214 0.595 0.412 0.474 24 0.140 0.283 0.412 0.663 0.050 … 0.594 0.050 0.412 0.283 0.412 25 0.794 0.794 0.594 0.950 0.594 … 0.717 0.663 0.050 0.412 0.717 26 0.794 0.717 0.794 0.412 0.140 … 0.794 0.717 0.412 0.050 0.140 27 0.950 0.950 0.717 0.950 0.283 … 0.950 0.794 0.950 0.950 0.050
Table 6 Total Relation Matrix
T 1 2 3 4 5 … 23 24 25 26 27 1 0.028 0.070 0.060 0.081 0.057 … 0.091 0.068 0.076 0.066 0.083 2 0.041 0.058 0.059 0.096 0.052 … 0.093 0.069 0.095 0.050 0.078 3 0.066 0.093 0.049 0.073 0.080 … 0.101 0.091 0.101 0.077 0.084 4 0.083 0.120 0.102 0.084 0.088 … 0.130 0.112 0.125 0.093 0.124 5 0.022 0.038 0.048 0.057 0.029 … 0.067 0.060 0.057 0.047 0.072 : : : : : : : : : : : 23 0.046 0.103 0.062 0.097 0.062 … 0.069 0.086 0.096 0.071 0.096 24 0.048 0.089 0.065 0.098 0.062 … 0.091 0.053 0.104 0.069 0.091 25 0.031 0.063 0.051 0.066 0.036 … 0.074 0.070 0.046 0.040 0.071 26 0.026 0.051 0.043 0.061 0.047 … 0.062 0.063 0.068 0.031 0.061 27 0.070 0.103 0.070 0.105 0.076 … 0.113 0.080 0.104 0.080 0.074
operations, balancing the tax burden among different enterprises, and encouraging competition.
Turkey’s position when it comes to the extent and effect of taxation is especially low (117th), whereas other leading crude steel producers such as China (41st), Japan (113rd), and the US (69th) are at higher levels [1].
Another policy implication is concerned with the level of efficiency of customs procedures which are related to the entry and exit of merchandise [1]. In the WEF reports (2012), the burden of customs procedures is considered to be one of the factors influencing the competitiveness of a country. According to the 2012–2013 report, Turkey is ranked 96th out of 144 countries. Other leading crude steel producers such as China (65th), Japan (36th), and the US (48th) are at higher levels in this category. This shows that there is an emerging need for Turkey to make improve-ments in this respect.
5 Conclusions
The competitiveness of an industry can be effectively improved if the factors that have primary importance and direct impact on its improvement are first specified. Therefore, in this study, a three-stage methodology has been proposed to specify the CSFs of the competitiveness of an industry, which is itself shaped by the global com-petitiveness of a country.
Table 7 Data for the causal
diagram ID Concepts Di Ri Di? Ri Di- Ri
8 Burden of customs procedures 2.165 0.740 2.905 1.426
18 Total tax rate 1.787 0.432 2.219 1.354
20 Extent and effect of taxation 1.872 0.730 2.601 1.142
1 Soundness of banks 1.951 1.045 2.996 0.906
27 Iron and steel production process sophistication 2.871 2.145 5.015 0.726 26 Iron and steel domestic market size 2.291 1.572 3.862 0.719
5 Trade tariffs 1.368 0.914 2.282 0.455
25 Iron and steel foreign market efficiency 2.539 2.145 4.684 0.395 22 Intensity of local competition 2.122 1.841 3.963 0.281 13 Extent of staff training 2.177 2.039 4.216 0.137 14 Reliance on professional management 1.843 1.739 3.582 0.104 2 Availability of scientists and engineers 2.065 1.987 4.052 0.078 7 Quality of overall infrastructure 1.999 1.929 3.928 0.070 24 Local supplier quantity 1.456 1.531 2.986 -0.075 21 Foreign direct investment and technology transfer 2.221 2.331 4.553 -0.110 3 Quality of railroad infrastructure 1.352 1.491 2.843 -0.140 9 Domestic market size index 1.717 2.008 3.724 -0.291 4 Foreign market size index 1.864 2.206 4.070 -0.341 19 Production process sophistication 2.050 2.406 4.456 -0.356 16 Availability of latest technologies 2.020 2.508 4.528 -0.487 23 Local supplier quality 1.525 2.171 3.696 -0.647 6 Quality of electricity supply 1.263 1.937 3.200 -0.674 10 Efficacy of corporate boards 1.796 2.483 4.279 -0.687 17 Firm-level technology absorption 1.764 2.477 4.241 -0.713 11 Quality of port infrastructure 1.278 2.182 3.459 -0.904 15 Nature of competitive advantage 1.869 3.017 4.886 -1.149
12 Extent of marketing 1.567 2.785 4.352 -1.218
The major contribution of this research lies in the development of linkages between various critical success factors influencing the Turkish iron and steel industry through a single systemic framework. By highlighting the relationships between these factors, it provides an impor-tant guide to the policymakers in preparing a strategic plan to promote success in the iron and steel industry. The methodology shows that the CSFs of the iron and steel industry in Turkey are Burden of customs procedures, Total tax rate, Extent and effect of taxation, and Soundness of banks. In fact, as discussed in the previous section, the obtained results are completely in agreement with the general perspective of industry experts. Therefore, it can be concluded that the results validate the accuracy of the proposed methodology.
The methodology used in this study is an integration of the Web-based survey, Delphi-type workshop, and fuzzy DEMATEL approaches to find the CSFs. A Web-based survey contributed to enlarge the spectrum of the analyses and to analyze the issue from different perspectives of the related stakeholders. The implementation of a Delphi-type workshop helped reveal and aggregate the opinions of the experts and finally fuzzy DEMATEL highlighted the criti-cal success factors based on those opinions. In fact, the DEMATEL method is based on graph theory that enables the division of multiple factors into cause and effect groups in order to better capture the causal relationships and con-vert those relationships between the critical factors into a structural model of the system. In this study, the expert opinions are given as linguistic terms resulting in vague and imprecise input to the DEMATEL approach. Therefore, a fuzzy set theory DEMATEL approach (i.e., Fuzzy DEMATEL) is well suited for this methodology. This study uses Wu and Lee’s (2007) fuzzy DEMATEL approach with an important modification [43]. The assessments of the decision-makers are first aggregated then defuzzified in order to avoid losing important information regarding the distribution of the individual assessments.
The proposed methodology is of widespread usefulness and applicable to any industry. Further research could involve its application in the specification of CSFs for other industries.
The basic limitation of the proposed model is that it is not statistically validated. As a further suggestion, SEM model could be initially used to find the causal factors, and DEMATEL could be applied subsequently to determine which factors are more important for the iron and steel industry. Such a combination might increase the perceived validity of the results. Finally, using DEMATEL along with a multi-attribute decision-making model fuzzy AHP, fuzzy analytical network process or fuzzy Choquet integral can also be utilized to deal with various relationships
between criteria and decide on their relative weights [55– 57].
Acknowledgments This research was supported by SEDEFED (Turkish Federation of Industrial Associations) and REF (TU¨ SI˙AD-Sabancı University Competitiveness Forum). The authors are also very grateful to all the experts who contributed to the surveys and to the anonymous referees for their invaluable suggestions.
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O¨ zgu¨r Kabak is an assistant professor at the Industrial Engineering Department of Istanbul Technical University (ITU), Turkey. He received his MS and PhD degrees in Industrial Engineering both from ITU, in 2003 and 2008, respectively. He had done postdoc research at Belgian Nuclear Research Centre (SCK•CEN) in 2009–2010 with the support of the Belgian Science Policy (BELSPO). He has publications in a variety of journals including the European Journal of Operational Research, Transportation Research Part A, IEEE Transactions on Knowledge and Data Engineering, and Journal of Global Optimiza-tion. His current research interests include fuzzy decision making and modeling complex systems.
Fu¨sun U¨ lengin is a professor of Operations Research and Decision Analysis. She was the Dean of the Management Faculty at Istanbul Technical University in 2002–2005 and the Dean of Engineering Faculty as well as the Head of Industrial Engineering Department at Dog˘us¸ University in 2009–2013. She has been the Dean of the School of Management at Sabancı University since 2003. Her research focuses on the multiobjective evaluation of macrosystems, in general, and transportation and logistics systems, in particular. Her areas ofinterests also include multi-attribute and group decision-making models, decision support systems, Bayesian causal maps, as well as neural networks. Her refereed articles have appeared in a variety of SCI and SSCI journals including Omega, Journal of the Operations Research Society, Socio-Economic Planning Sciences, European Journal of Operational Research, and Transportation Research-E. She is in the Editorial Board of ‘‘Transportation Policy’’ and ‘‘Case Studies in Transport Policy’’ journals. She is the member of the Scientific and Steering Committees of World Conference of Trans-portation Research Society (WCTRS). She has been appointed as the international program chair of many international and national conferences. She is currently the advisor of the Transportation and Logistics sector in the Turkish Union of Chambers and Commodity Exchanges (TOBB).
Bora C¸ ekyaywas born in I˙stanbul, Turkey, on April 25, 1978. He received his BS and MS degrees in Industrial Engineering from I˙stanbul Technical University in 2003 and 2005, respectively, and PhD degree in industrial engineering and operations management from Koc¸ University in 2009. He has been working as an assistant professor of Industrial Engineering at Dog˘us University since 2011. Prior to that, he was a postdoctoral research fellow at Desautels
Faculty of Management, McGill University, Canada. His current research interests include system reliability analysis of complex systems, maintenance planning and optimization, and transportation of dangerous goods. He has published articles in European Journal of Operational Research, IIE Transactions, IEEE Transactions on Reliability, and Annals of Operations Research.
S¸ule O¨ nsel is an associate professor in the Industrial Engineering Department, Dogus University, Istanbul, Turkey. She graduated from Istanbul Technical University (Management Faculty, Department of Industrial Engineering) in 1993 and received her MSc degree in 1995. She pursued her PhD education also at Istanbul Technical University (2002). Her PhD thesis was on the scenario analysis of inflation in Turkey with the help of artificial neural networks. Her research topics are decision making, neural networks, scenario analysis, cognitive mapping, and Bayesian causal maps. Her refereed articles have appeared in a variety of journals including Expert Systems with Applications, Transportation Research Part C, Socio-Economic Planning Sciences, European Journal of Operational Research, and International Journal of Production Research.
Ozay Ozaydin is an Assistant Professor at Dogus University, Engineering Faculty, Industrial Engineering Department. He received his BSc degree in Aeronautical Engineering, MSc degree in Engineering Management, and PhD degree in Industrial Engineering, all the three from Istanbul Technical University. He participated in various projects for Istanbul Metropolitan Municipality, the World Bank, and Competitiveness Forum. His research areas are decision making, emergency management, and product and process design.
