Logistics center site selection by ANP/BOCR analysis: A case study of Turkey

Abstract

Site selection for logistics centers is one of the most strategic decisions in logistics. This study addresses the problem of logistics center site selection, using Analytic Network Process/Benefits, Opportunities, Costs and Risks, which is a multi-criteria decision making technique. The decision problem is applied to the city of Trabzon, which is an important city of the Eastern Black Sea Region in Turkey. First, the results are evaluated in terms of each sub-network and each group and, next, are consolidated for each alternative. They yield that Alternative A is the most appropriate logistics center location of all in Trabzon. Furthermore, the sensitivity analysis results emphasize that the decision model is robust.

Keywords

Logistics center site selection Analytic Network Process (ANP)benefits opportunities costs and risks (BOCR)

1 Introduction

In recent years, economies of scale and reduced transportation costs have increased attention to logistics centers [1]. A logistics center is the hub of a specific area where all the activities relating to transport, logistics and distribution are performed on a commercial basis by various operators [2]. It also reflects a modern way of storage areas, truck services, offices, banking, postal and insurance services and customs infrastructures in certain cases [2]. The logistics center is also vital in minimizing traffic congestion arising the movement of goods in urban areas [3]. In the literature, there are some studies that take into consideration the characteristic and economic contribution of logistics centers such as [4 –7].

Site selection for logistics centers is considered as one of the most strategic decisions in the logistics literature [8]. The problem of logistics center site selection has two remarkable aspects [9]: Firstly, the quality solution to the problem has an impact on the social benefit of logistics rationalization and on the commodity circulation of the overall system as well. Secondly, since the investment in a logistics center is costly, the solution may not bring the anticipated effect, wasting resources and thus, entrepreneurs probably will suffer losses.

It is well known that determining logistics center location involves both quantitative and qualitative factors, which directly affect the decision [1, 10]. In this regard, these factors should be taken into consideration simultaneously during the decision-making process [10]. In the literature exist a lot of methods to realize a logistics/ distribution center site selection model such as Analytic Hierarchical Process [11 –14], TOPSIS [1 , 15–17], Artificial Neural Network [18], Genetic Algorithm [19, 20] and Fuzzy Logic [12 , 21–23]. The above-mentioned approaches offer solely procedural systems for problem solving and they exclude the feedback and interdependency relationships between decision attributes and alternatives [24]. However; Analytic Network Process (ANP)/ Benefits, Opportunities, Costs & Risks (BOCR) supply these relationships [25, 26].

Site selection includes not only benefits and opportunities but also costs and risks; therefore, it needs to consider the benefits, opportunities, costs and risks of alternatives. Locating logistics centers concerns multi-actors such as academicians, public institutions and organizations related to logistics, logistics service providers, manufacturing firms and non-governmental organizations. Although considered in a lot of academic studies, multi–actor views have not been included in the analyses until now. Therefore, this paper is the first attempt to engage multi-actor views in a study.

The paper addresses the problem of logistics center site selection, using ANP/BOCR. A case study is conducted in the city of Trabzon in Turkey. It aims to show the validity of the proposed methodology in solving the problem of suitable logistics center site selection. The study mainly contributes to the literature through the ANP/BOCR method in logistics center selection for the first time.

The rest of the paper is structured as follows: Section 2 describes the related literature, Section 3 the ANP/BOCR method and, Section 4 the proposed methodology and the ANP/BOCR application. Section 5 yields the results of the analysis and the final section presents the main findings, discussions, conclusions and suggestions for future studies.

2 Literature review

The literature review as regards logistics centers is composed of two main groups: studies on economic effects & advantages of logistics centers and studies on selection of optimal location for logistics centers.

The studies in the first group are summarized as follows: [27] determined the optimal allocation and size of seaports from the point of view of national economy. [28] presented a model to assess the feasibility of a new freight village financed by private and public investments. [29] evaluated the requirements of an international logistics distribution center in Taiwan. [30] concentrated on some approaches to various logistics centers according to the country and purpose. [31] presented the evolution of a logistics park in Slovak Republic, applying SWOT analysis. [2] considered the development of a freight village in Thriasio, an industrial area found near Athens. [32] dealt with the concept of logistics center and its benefits for users. [33] provided a literature review on economic effects and advantages of logistics centers. [34] investigated the progress of logistics centers in Latvia. [35] laid stress on the advantages of logistics centers such as reduction in inventory costs and provision of information transparency during material flow and product distribution. [36] focused on some Chinese and French experiences of freight villages, throwing light on the major stakes and questions of introducing logistics to an urban and regional planning agenda. [37] suggested some strategic tasks of developing a logistics center successfully, touching some efficient samples in the world. [38] studied the development of logistics center in Central Asia. [39] sought to design a hybrid system for a resource information management in humanitarian logistics centers. [40] described the role of logistics center including intelligent solutions in a multimodal transport system. [41] presented the role of seaports as integrated logistics centers in the development of sustainable distribution of goods in urban areas. [42] emphasized that relief logistics centers and their service quality matter highly when natural disasters happen.

The second group in the literature review is as follows: [43] described a mathematical model for the determination of the optimal size and location of public logistics terminals. [1] proposed a method to solve the distribution center location problem under fuzzy environment. [44] introduced a geographic information system-aided process to a warehouse site selection decision. [45] used a method which combines the Fuzzy Analytic Hierarchical Process and Artificial Neural Networks in order to select the optimal site for a warehouse in Taiwan. [46] chose the optimal location of industrial and storage facilities, combining Component Object Model, Geographic Information Systems and AHP. [47] analyzed a transport project through AHP, taking into account stakeholders in the freight transport sector. [48] selected the optimal location for a logistics center in the Region of El Paso, applying Fuzzy TOPSIS. [24] employed a fuzzy comprehensive evaluation algorithm to optimize the location selection of a logistics center. [49] sought an approach that enables to determine a unique solution to a logistics center location problem at uncertain costs by using fuzzy sets. [50] developed a fuzzy integrated hierarchical decision making approach to solve the distribution center location selection problem. [51] analyzed the optimal location of a warehouse by using multi-objective decision-making techniques. [9] analyzed the current domestic and international research on the evaluation of location selection of logistics centers, combining fuzzy AHP and TOPSIS. [8] sought the optimal location for logistics distribution centers via bi-level programming. [52] determined the optimal location of international distribution centers by means of a mathematical model. [3] first evaluated the criteria through fuzzy theory and next, determined the potential locations for an urban distribution center through fuzzy TOPSIS. [53] proposed a new hybrid method - Fuzzy DEMATEL & fuzzy AHP/ANP- to the location problem of an international distribution center. [16] presented a comprehensive methodology by utilizing Axiomatic Fuzzy Set and TOPSIS for the selection of a logistics center location. [10] proposed a new hybrid heuristic algorithm, a combination of rough set methods and fuzzy logic, so as to tackle another distribution center location problem. [11] evaluated three alternative locations for a large hub-port in South Africa, using multi-criteria analysis. [54] evaluated the logistics distribution center location problem through Genetic Algorithm. [12] examined the optimal location of a logistics center in China, using AHP and Genetic Algorithm. [21] proposed hybrid fuzzy AHP method to the location options of international distribution centers. [55] combined the hybrid optimization algorithm with Genetic Algorithm & Tabu Search in the site selection of a modern railway logistics center. [14] aimed to determine the appropriate freight village candidate in Istanbul by using both AHP andPROMETHEE.

The ANP/BOCR technique is used to make a decision with regard to benefits, opportunities, costs and risks. This technique was applied to different areas such as production system selection [56], supply chain design [57], raw material procurement [58], evaluation of high-tech alternatives [59], manufacturing execution systems [60], supplier selection [61], priority determination in strategic energy policies [62], locating undesirable facilities [26], project selection [63], production line selection [64], location problems of a new waste incinerator plant [65], assessment of the best supply chain distribution strategy [66], third party logistics service provider selection [67], dispatching rules selection in Flexible Manufacturing System [68] and analysis of supply chain performance [69]. The literature review indicates that no study on logistics center site selection has been conducted through the ANP/BOCR method. Therefore; a location selection procedure is presented in our study to construct a logistics center, using ANP/BOCR. In this context, a case study is prepared for the city of Trabzon in Turkey. The study certifies the validity of the proposed methodology in solving the problem of logistics center site selection.

3 Analytic Network Process (ANP)/Benefits, Opportunities, Costs and Risks (BOCR)

Analytic Network Process (ANP) is the generalization of the Analytic Hierarchical Process (AHP) by taking into consideration the dependence between the elements of the hierarchy [70]. It makes that ANP is more realistic when compared with AHP [71]. In fact, many of the real world decision problems cannot be structured hierarchically because they comprise an interaction and dependence between higher-level elements and lower-level elements in a hierarchy [72]. Therefore, ANP is represented by a network rather than a hierarchy [70]. Contrary to AHP, ANP maintains a more generalized model in decision-making without making assumptions on the independency of the higher-level elements within a level [73].

ANP enables both interaction and feedback within the clusters of criteria (inner dependence) and between the clusters (outer dependence) [26]. If there is an arrow from cluster i to cluster j, cluster i will be called as controlling cluster and cluster j as controlled cluster. The criteria in a controlling or controlled cluster are called as controlled or controlling criteria [63]. Controlled clusters/criteria must be pairwise compared to their corresponding controlling cluster/criterion in terms of importance. That is why comparisons must be formed according to the controlling one [71].

Prior to pairwise comparisons, all compared criteria and clusters are linked to each other. There are three types of connections: one-way connection (if there is only one-way connection between two clusters, only one-way dependences exist and such a situation is represented with direct rows), two-way connection (bi-directed arrows are used) and loop connection (it expresses the comparisons in a cluster and inner dependence) [26]. Generally, decision makers/experts have to complete two types of pairwise comparisons: one is the cluster level (if there are sufficient clusters to do so) and the other is the criterion/alternative level [63]. Depending on the 1–9 scale recommended by Saaty, the comparisons are made as shown in Table 1 [72].

The process of ANP/BOCR needs to analyze a decision according to Benefits (B), good things that would be associated with making the decision; Opportunities (O), potentially good things that could emerge in the future, resulting from taking the decision; Costs (C), pains and disappointments that would be connected with taking the decision; and Risks (R), potential pains and disappointments that could be associated with making the decision [70].

4 The proposed methodology and ANP/BOCR application

4.1 The proposed methodology

In this study, a general decision model is proposed that it considers four sub-network- B, O, C and R. The four sub-networks are comprehensively assessed such that they deal with short and long-term, obvious and potential, positive and negative, tangible and intangible attributes of outcomes [63]. The basic steps in the application process of the proposed methodology are shown in Fig. 1.

First of all, correlations matrices are prepared in each network, showing the interactions between the clusters and elements. Then, an ANP/BOCR survey is prepared according to the correlations matrices. The survey includes pairwise comparisons of the clusters and criteria. For all pairwise comparisons, lots of face-to-face interviews are held with the decision makers/experts and a comprehensive questionnaire is applied to them (Appendix A). In the survey, the pairwise comparisons are made by the decision-makers/experts who have competency and experience about the decision problem. The decision makers/experts groups are presented in Table 2.

4.2 ANP/BOCR application

4.2.1 Determining the problem

The decision problem of this study is to determine the optimal logistics center site selection in Trabzon, Turkey, which is the most highly populated city in the Eastern Black Sea Region. It has an increasing importance as an international trade center and a bridge between the Middle East and Commonwealth of Independent States. Trabzon has some advantages (modern port, international airport and free zone etc.) and a very important disadvantage (lack of railway transportation) in terms of being a logistics center. However, the government plans to resolve this deficiency as soon as possible.

4.2.2 Determining the criteria

In the determination of the criteria for logistics center site selection, it is faced with lack of studies in the related literature. Therefore, other types of site selection are also reviewed. Based on an extensive literature review and the decision makers/experts, the criteria in the ANP with the BOCR model are determined. The criteria can be shown in Table 3.

At this point, it can be said that another contribution of the study is that it includes some of the criteria that have not been reviewed in the literature (flexibility in meeting customer needs, handling cost etc.). Additionally, some criteria such as railway projects, the Zigana and Ovit Gateways are included by the groups of decision makers/experts because they are specific for Trabzon.

4.2.3 Determining the alternatives

The alternatives are determined by the decision makers/experts, presented in Table 2. In this phase, six alternatives coded as A, B, C, D, E and F are evaluated. However, Alternatives D, E and F are eliminated due to some shortcomings (lack of expansion opportunities, distance from the port, airport and city center etc.). At the end of the evaluation, Alternatives A, B and C are found appropriate to examine in the study.

Some features of the alternatives can be stated as follows:

Alternative A has port and highway connections. It is 6 km away from the airport, 2 km from the Zigana Gateway and 55 km from the Ovit Gateway. There is no sufficient expansion area.

Alternative B is the nearest site to the production center. It has a highway connection, which is located 18 km away from the airport and 24 km from the port. Alternative B is 26 km away from Ovit and Zigana gateways. It holds a sufficient expansion area.

Alternative C has a highway connection. It is 40 km away from the port, 33 km to the airport and 18 km to the production center of Trabzon. It has 8 km to the Ovit Gateway and 37 km to the Zigana Gateway. It also holds a sufficient expansion area.

4.2.4 Constructing the ANP/BOCR model

After determining the criteria and alternatives, the ANP/BOCR model is constructed as shown in Fig. 2. First, the geometric means of the answer to each pairwise comparison is calculated and next, an nxn matrix is obtained. The matrix constitutes a complex system which is difficult to solve by using a spreadsheet program. For this reason, Super Decision 2.0.8 software is used to solve and analyze themodel.

The model consists of two stages. Our goal, which is to select the best site selection for logistics center in Trabzon, is placed at the top of the model. Benefits, opportunities, costs and risks are determined as four sub-networks and placed at the second stage of the model. In Table 3 lie the clusters and elements of each sub-network in detail. The alternative cluster is found in all sub-networks because the aim of the study is to select the best alternative.

5 ANP/BOCR application results

5.1 General results

Completing all pairwise comparisons, their consistency ratios are calculated. As known, the consistency measure is used to determine possible errors in judgments. If the inconsistency ratios are less than 0.1, all comparisons are consistent and expert judgments are reliable [77]. In our study, since the inconsistency ratios of all the comparison matrices are less than 0.1, all judgments are accepted as reliable. The final relative weights of the clusters and criteria are shown in Table 4.

As can be seen in Table 4, Alternatives A and B are equally important in the benefits sub-network. DPC and TE are the most important criteria in the cluster of location and socio-economic factors respectively. In the opportunities sub-network, Alternative A is the most suitable site for logistics center location. In terms of railway and highway transportation, Project 1 and ZG are the most important criteria respectively. Alternative B is the most appropriate site according to the costs sub-network. The criteria in the cluster of land cost are equally important. However, BFC and HC are the most important criteria in the cluster of facility and operation costs respectively. Finally, in the risks sub-network, Alternative B is the most suitable site, SL is the most important criterion in terms of physical characteristic of land and ALRP is the most important criterion in the structure and ownership of theland.

5.2 Group results

In this study, the findings for each sub-network are comparatively evaluated by each decision making group. They can be seen in Table 5.

As can be seen in Table 5, in the benefits sub-network, DH is the most important criterion for academicians and manufacturing firms, while it is DPC for the other groups. Furthermore, Alternative A is the most suitable location according to academicians and manufacturing firms, while B for theothers.

The results of the opportunities sub-network indicate that ZG is the most important criterion for all groups except for the academicians. Moreover, A is the most suitable alternative location for all groups except for the manufacturing firms.

HC is the most important criterion for all groups according to the results of the risks sub-network. Alternative A is the most suitable location for three groups; academicians, public institutions & organizations and logistics service providers, while Alternative B for manufacturing firms and non-governmental organizations.

Finally, in the costs sub-network, SL is the most important criterion for all groups except for the academicians. Besides, Alternative B is the most suitable location for all groups except for public institutions and organizations.

5.3 Combined results

As can be seen in Table 4, Alternative A is the most appropriate logistics center location by the benefits and opportunities sub-networks. Alternative B is the best of all by the costs and risks sub-networks. In order to resolve this complexity, all results obtained for each sub-network must be consolidated. In this respect, Saaty proposes two ways to perform the task [63]: The first one is the multiplicative method with the BO/CR formula. According to this formula, the values of the benefits and opportunities sub-networks are firstly multiplied and next, are divided by the multiplication of the values of the costs and risks sub-networks. It implies that BOCR sub-networks are equally important, which is not always true in practice.

The second method is the subtractive method and shown in bB + oO – cC- rR formula where b, o, c and r are the priorities for the BOCR sub-networks. This formulation is used for determining the relative weights and ranking scores for the location alternatives. It is more preferred because it gives more accurate results in application. During the meetings made with the decision makers/experts, it is decided that all sub-networks cannot be equally-weighted in importance. Therefore, the subtractive method is preferred in our study. After completing the above steps, BOCR weights are calculated by the strategic criteria- economic and social impact- and are found 0.26, 0.32, 0.27 and 0.15 respectively. After the calculations, the alternative weights in BOCR are determined as in the Table 6.

Alternative A is the most appropriate logistics center location for Trabzon by all groups, as can be seen in Table 6. Furthermore, the ranking of the alternatives is A, B and C respectively, about which all groups agree.

5.4 Sensitivity analysis

The sensitivity analysis is used to evaluate the stability of the result and reflect the robustness of the model [78]. In this study, we change the priorities for the BOCR sub-networks one at a time to perform the sensitivity analysis. To this end, ten different scenarios are prepared as shown in Fig. 3. In the analysis, scenario 1 indicates the current state, scenario 2 the equal importance of the sub-networks and scenario 3 the replacement of the minimum weights and maximum weights of the sub-networks, while the other scenarios indicate the alteration of the entire network weights.

The ranking of the alternatives remains the same for all scenarios by the results of the sensitivity analysis. It can be concluded that the result is stable. In other words, our decision model is robust and Alternative A is the best logistics center location for Trabzon.

6 Discussion and conclusion

The purpose of this study is to introduce a comprehensive decision methodology for the selection of the most appropriate logistics center site. This study differs from the others in two aspects: Initially, the paper is probably the first attempt to apply the logistics center site selection with ANP/BOCR in the literature. Secondly, it includes multi-actor views in the decision making process of logistics center site selection.

Logistics center site selection is a multi-criteria decision making problem, which should involve both qualitative and quantitative factors from the point of view of various groups such as academicians, public institutions and organizations, logistics service providers, manufacturing firms, and non-governmental organizations. For this purpose, multi-actor views are included in the process and ANP/BOCR is used for decision making, which enables the evaluation of both qualitative and quantitative factors and takes into consideration the dependent and independent relationships between the criteria. Taking into account the interaction of the higher level elements and lower level elements, the study allows BOCR analysis ofthe realization of the desired objective.

The decision making groups choose the different alternatives because each alternative has advantages over the others in each sub-network. The advantages of Alternative A can be stated as (i) port, (ii) connection with the Black Sea Coast Road and (iii) the nearest point to the airport and Zigana Gateway. These advantages are effective for Alternative A to be selected as the most suitable site in the benefits and opportunities sub-networks. The advantages of Alternative B are (i) appropriate field, (ii) connection to motorway and (iii) the nearest point to production center. From this point of view, Alternative B is the most suitable site in the risks and costs sub-networks. Finally, Alternative C has advantages such as (i) connection to motorway, (ii) the nearest point to the Ovit Gateway. Alternative C is the least suitable logistics center site for all sub-networks. In the selection of the best alternative steps, the subtractive method, recommended by Saaty, is chosen and Alternative A is found the most suitable logistics center site for Trabzon. According to the sensitivity analysis, the ranking of all the alternatives keeps the same and stable. Alternative A has a connection to the port and motorway. It is 6 km and 2 km away from the airport and the Zigana Gateway respectively. However, its expansion possibilities are low since it is very close to the city center.

According to the benefits sub-network, traffic effect is one of the most important criterion. This result is in line with the findings of the previous studies such as [9 , 79]. On the other hand the distance to airport is the least important criterion.

Trabzon is linked to the Eastern and Southeastern Regions of Turkey with the Zigana and Ovit Gateways. They are the most important regions for Trabzon in terms of load flow. The Zigana Gateway is more preferred than the Ovit Gateway in terms of geographical conditions. For this reason, the Zigana Gateway is the most important criterion in the opportunities sub-network. Project 1 is the most important railway project by the decision makers/experts. The criteria in this sub-network are specific for Trabzon.

Handling cost is the most important criterion for the cost sub-network, which is parallel with the results of the studies by [1 , 80], while shipping cost is the least important criterion.

Slope of land is the most important criterion for the risks sub-network, which also parallel the results of [24 , 53], and the other criteria are of equal importance.

The proposed model provides a generic framework to guide managers who want to establish a logistics center. It is also applicable to logistics center site selection in other locations through slight modifications. The results of the analysis point out that the ANP/BOCR model handles the real problems of a logistics center site selection. Last but not least, the model offers a more reliable and accurate analysis, considering the interdependency relationships among the decision factors. On the other hand, it needs more time and effort because each interdependency relationship geometrically multiplies the number of pair-wise comparisons.

Finally, there are some limitations in this study. Firstly, it does not consider all possible clusters, criteria that can be added to the model. And each actor’s contribution in the decision making groups is equally weighted in the study. It can be differentiated and thus, the results of different weight combinations can be determined. In future researches, different multi-criteria decision making methods (Grey Relational Analysis, ELECTRE, TOPSIS etc.) and fuzzyMCDM methods can be utilized for the logistics center site selection for Trabzon and compared with the findings in this study.

Footnotes

Acknowledgments

This study is funded by Karadeniz Technical University under project number 2010.115.002.4.

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