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Efficiency analysis of organized industrial zones in Eastern Black Sea Region of Turkey
Accepted Manuscript Efficiency analysis of organized industrial zones in Eastern Black Sea Region of Turkey Mehtap Dursun, Nazli Goker, Burcu Deniz Tulek PII:
S0038-0121(18)30142-3
DOI:
https://doi.org/10.1016/j.seps.2018.10.010
Reference:
SEPS 659
To appear in:
Socio-Economic Planning Sciences
Received Date: 7 May 2018 Revised Date:
23 October 2018
Accepted Date: 28 October 2018
Please cite this article as: Dursun M, Goker N, Tulek BD, Efficiency analysis of organized industrial zones in Eastern Black Sea Region of Turkey, Socio-Economic Planning Sciences (2018), doi: https:// doi.org/10.1016/j.seps.2018.10.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Efficiency Analysis of Organized Industrial Zones in Eastern Black Sea Region of Turkey Mehtap Dursun*, Nazli Goker, Burcu Deniz Tulek Industrial Engineering Department, Galatasaray University, Ortakoy, Istanbul 34349, Turkey
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E-mail addresses: [email protected]; [email protected]; [email protected]
*
Corresponding author: Mehtap Dursun, Galatasaray University, [email protected]
ACCEPTED MANUSCRIPT Efficiency Analysis of Organized Industrial Zones in Eastern Black Sea Region of Turkey
Abstract
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Organized Industrial Zones (OIZs) are the production areas of goods and services that are established to provide planned industrialization and planned urbanization by structuring the industry in suitable areas, to prevent environmental problems, and to provide efficient use of
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resources. The aim of this study is to propose a decision-making model based on data envelopment analysis (DEA) to evaluate the relative efficiencies of OIZs located in the
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Eastern Black Sea Region of Turkey. First, the efficiency score of each alternative is determined by employing a DEA formulation with interval data. Second, a common weight DEA-based formulation is applied in order to obtain common set of weights and provide ranking results with an improved discriminating power in imprecise nature. In this study, the
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best performing OIZ alternative, which performs in Eastern Black Sea region of Turkey, is identified based on the approach proposed by Salahi et al. (2016). The DEA-based model developed by Salahi et al. (2016) is improved by modifying a constraint. In order to
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demonstrate the robustness of the application, two numerical illustrations are given. The first example compares the results obtained by the formulation addressed in Salahi et al. (2016)
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and the improved model; while second numerical illustration provides a case study conducted in Eastern Black Sea region of Turkey. Keywords: Data envelopment analysis, efficiency analysis, imprecise data, organized industrial zones
ACCEPTED MANUSCRIPT 1. Introduction
Organized Industrial Zones (OIZs) are important structures of the industry and they began to be established and supported by the Turkish State since 1960s. At the present time there are
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one or more OIZs in all cities of Turkey. The low occupancy rates of these OIZs make the question of whether the OIZs work efficiently. Although the need to evaluate the efficiencies of Turkey’s OIZs is stated on many sources, the studies related on this area are quite few and
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insufficient.
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The aim of this study is to propose a decision-making model based on data envelopment analysis to evaluate the relative efficiencies of OIZs located in the Eastern Black Sea Region of Turkey. The application procedure is based on Salahi et al.’s approach. A limitation in a constraint of the model proposed by Salahi et al. (2016) is detected and this constraint is
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reorganized. Salahi et al. (2016) suppose that the weighted sum of the lower bounds of the outputs is smaller than the weighted sum of the upper bounds of the inputs, however, this assumption yields efficiency scores that are greater than one, which contradicts principal
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notion of DEA. This contradiction is eradicated by reformulating the related constraint. The results of the formulation developed by Salahi et al. (2016) and the improved model are
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compared, and the efficiency scores of OIZ alternatives are identified via solving the improved model. The final efficiency values and ranking results are obtained by solving a common weight DEA-based programming model proposed by Salahi et al. (2016).
The rest of the paper is organized as follows. The following section presents OIZ public policy in Turkey. Section 3 gives a brief literature review on the use of DEA methods to evaluate the industrial zones’ or industrial zone’s firms’ efficiency. In Section 4, materials and
ACCEPTED MANUSCRIPT methods are presented. Results and discussions are provided in Section 5 and Section 6, respectively. Finally, concluding remarks and directions for future research are given in the
2. Organized industrial zone public policy in Turkey
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last section.
Organized industrial zones are structures that provide collective opportunities to the firms and
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aim to reduce the costs and facilitate the business of the companies with the special services they provide and thus contribute to the strengthening of the industry. These structures are
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supported by the government and investors are encouraged to prefer organized industrial zones. One of the objectives of the organized industrial zones is to encourage the backward regions without creating regional imbalances and to attract the industrial investments to these regions. For this reason, it is important to evaluate the status of organized industrial zones in
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regions with low level of development.
Organized Industrial Zones are the production areas of goods and services that;
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• are established to provide planned industrialization and planned urbanization by structuring the industry in suitable areas, to prevent environmental problems, to
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provide efficient use of resources,
• are equipped with necessary administrative, social and technical infrastructural areas and small manufacturing, repair, trade, education and health fields, technology development zones, and operated according to the provisions of “Organized Industrial Zones Law”.
ACCEPTED MANUSCRIPT Industrialization efforts in Turkey increased with the establishment of Republic and were seen as the base of economic development and newly acquired political independence. Industrialization was left to private sector initiatives in the first years of the Republic, but due to lack of financial power and lack of experience, the private sector could not fulfill this task
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as expected. Following the first efforts at industrialization, industrial infrastructure work was started within the frame of the "I. Five-Year Industrial Plan", which had been put into effect in 1931. And in the period of planned development, which started in 1960, it was clearly
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stated that the industry is the "locomotive" sector.
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Organized Industrial Zones and Small Industrial Sites have been started to be established by the government in 1960s for planned industrialization and planned urbanization (Cansiz 2010). In Turkey, OIZ implementations were first started with the establishment of an OIZ in Bursa in 1962.
In order to reduce regional development disparities and to provide regional
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development “Eastern Anatolia Project Regional Development Administration”, “Eastern Black Sea Project Regional Development Administration” and “Konya Plains Project Regional Development Administration” were established. The development graphs of
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1 and Figure 2.
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completed OIZs (as number and area) in Turkey over the years since 1990 are given in Figure
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Figure 1. Completed OIZs over the years on number basis (http://www.sanayi.gov.tr)
Figure 2. Completed OIZs over the years on hectare basis (http://www.sanayi.gov.tr)
All of the OIZs’ monitoring and evaluation operations are carried out by Ministry of Science, Industry and Technology in Turkey.
According to Ministry of Science, Industry and
Technology’s 01.03.2017 dated data in 297 OIZs and in 49.877 industrial parcels have been started production and 1.658.835 employments are provided. However, in existing OIZs there are also idle parcels except the parcels in production, and in some OIZs the ratio of idle
ACCEPTED MANUSCRIPT parcels to total parcels is quite high. Total zone size, number of parcels, employment and occupancy rates for the OIZs in Turkey are given in Table 1. Table 1. Turkey’s OIZs’ total data 89.160
Industrial Parcel Size (Ha)
51.126
Number of Parcels (Zoning)
75.734
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Zone Size (Ha)
Number of Parcels (Zone)
71.263
Total Number of Parcels
77.792
Size of Assigned Parcels (Ha) Number of Idle Parcels
58.144
39.771
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Number of Assigned Parcels
13.119 11.355
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Size of Idle Parcels (Ha) Number of Parcels in Production
49.877
Number of Parcels in Construction
3.414
Number of Parcels in Project
4.853
Employment Estimated Employment
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Occupancy Rate (Number of Assigned Parcels / Number of Parcels (Zone))
Rate of Parcels in Production (Number of Parcels in
2.409.323 68%
43%
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Production / Number of Parcels (Zone))
1.658.835
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As seen in Table 1, the average occupancy rate (Number of Assigned Parcels/ Number of Parcels) of the Turkey’s OIZs is 68% and cannot be said to be a high rate. On the other hand, the rate of parcels in production (Number of Parcels in Production/Number of Parcels) is lower and only 43%. When these rates are considered, it should be questioned whether the OIZs reach the desired efficiency.
ACCEPTED MANUSCRIPT 3. Literature review
Organized Industrial Zones are the production areas of goods and services that are established to provide planned industrialization and planned urbanization by structuring the industry in
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suitable areas, to prevent environmental problems and to provide efficient use of resources. Organized industrial zones allocate private sector investments to specific regions in order to balance regional development. Moreover, they meet land requirements of developing
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industries and support industrial production according to a specific program, hence they create an external economy. Besides, organized industrial zones contribute to national development
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goals by encouraging underdeveloped regions to where they invest for obtaining regional equality. They become instruments of development by providing spatial planning, manage and activate the efficiency of industrial production of underdeveloped regions. Thus, effective public policies should be implemented on OIZs. Recently, OIZs with low occupancy rates,
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which cannot provide the expected industrial development, are established due to the wrong investment decisions that have been made in order to eliminate regional inequalities, prevention of uneven growth in the country and long-term economic efficiency. However, it is
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necessary to take the right decisions to obtain the expected benefits from OIZs.
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Many governments support the implementation of OIZs due to their economic benefits. However, the low occupancy rates of these OIZs make the question of whether the OIZs work efficiently. Although the need to evaluate the efficiencies of OIZs is stated on many sources, the studies related on this area are quite few and insufficient. This section summarizes some significant studies about the use of DEA methods to evaluate the industrial zones’ or industrial zone’s firms’ efficiency.
ACCEPTED MANUSCRIPT Most of the studies deal with environmental impact. Hu et al. (2009) applied DEA to analyze the performance of 57 industrial parks in Taiwan from 2000 to 2004. DEA approach was used to compute the overall technical efficiency (OTE), pure technical efficiency (PTE), and scale efficiency (SE) scores. In the study the discrepancy efficiency was compared and the
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benchmark in all industrial parks was listed. Moreover, this paper intended to understand the directions of area economic development by analyzing the influence of environmental factors toward the efficiency of Taiwan’s industrial parks. Hu et al. (2010) used the four-stage DEA
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proposed by Fried et al. (1999) to evaluate the performances of science and technology industrial parks (STIPs). The data related to 53 China STIPs from 2004 to 2006 were The four-stage DEA was implemented by taking environmental factors into
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regarded.
consideration besides input and output variables and to provide an accurate assessment of the STIPs’ efficiencies. Khodakarami et al. (2014) proposed a gradual efficiency improvement DEA model and performed an application of the model using 31 Iranian industrial parks data.
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It was aimed to analyze the sustainability of industrial parks in this study. Environmental performance was used to determine follower and pioneer DMUs and it is tried to direct the follower DMUs toward pioneer DMUs to gradually increase their performance and plan how
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to achieve this improvement. Chen et al. (2017) evaluated environmental efficiency of 11 provinces and 131 cities in the Yangtze River Economic Zone in China proposing an
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approach based on super efficiency DEA and Malmquist index methods. Fan et al. (2017) assessed eco-efficiencies of 40 industrial parks in China by employing DEA methodology.
The studies that cope with macroeconomic results are as follows. Yenilmez and Girginer (2012) examined the efficiency of exporter textile firms’ in Eskisehir Organized Industrial Zone by implementing DEA which aims output maximization. 5 exporter firms’ input and output data for 2008 and 2009 was used to measure of the relative efficiencies of the firms.
ACCEPTED MANUSCRIPT Izadikhah and Farzipoor Saen (2015) proposed a new DEA approach to rank all efficient DMUs. The average of inefficient DMUs was defined as the virtual DMU. To overcome the drawbacks of the similar developed models, which cannot differentiate the efficient DMUs, the influence of efficient DMUs on both virtual DMU and other efficient DMUs were
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considered in the proposed model. Developed new approach was applied to the 17 industrial parks in Iran.
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The inputs and outputs, and also the employed model and obtained results of these studies are
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indicated in Table 2.
Table 2. Studies used in the literature review Ref.
Aim of the Study
Criteria Inputs
Capital stock
Number of workers
Results
CCR-DEA and BCC- Product-type and DEA models transportation are the two key factors of industrial park efficiency.
Number of firms
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(Hu et al. 2009)
To measure the efficiency of industrial parks in Taiwan
Model
Area of the firms
Utility maintenance fee Output
Operating Value
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Inputs
(Hu et al. 2010)
To investigate the efficiency of STIPs in China
(Yenilmez and Girginer 2012)
To analyze the efficiency of exporter firms in Eskisehir OIZ
Input-oriented DEA model
Number of firms set up in STIP Number of employees work in STIP Percentage of employees graduated from university to total employees Expenditure in R&D of STIP Percentage of science and technology personnel to total employees Outputs Technical and patent revenue of STIP Products sales revenue of STIP Commodity sales revenue of STIP CCR Inputs Production amount Number of employees
The environmental factors do affect the efficiency of the STIPs, especially in the west area.
Inefficient companies should decrease their production quantities whereas they have to
ACCEPTED MANUSCRIPT increase the amounts of the export values.
Net assets of the firms Output Export values
Material cost Labor cost Investment Area of operation Intermediate Output/Inputs (Khodakarami et al. 2014)
To analyze the sustainability of industrial parks in Iran
Created companies Productions Environmental projects Outputs
Revenue
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Number of graduates
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Energy cost
2 stage DEA based Assessing sustainable network model performance of industrial parks and planning to improve performance are essential activities.
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Inputs
Number of active companies Effluent
CO2 emission
Welfare services Inputs
Number of workers
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Population Land area
(Izadikhah and Saen 2015)
To measure the efficiency of industrial parks in Iran
Working capital
A novel DEA-based Finds the efficiency technique for fully scores of 17 Iranian ranking all DMUs industrial parks based on changing reference set using a single virtual inefficient DMU.
Outputs
Number of signed contracts
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Volume of investment Number of created jobs Total revenue
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To assess environmental efficiency of 11 provinces and 131 cities in the Yangtze River Economic Zone in China
(Chen et al. 2017)
Inputs Labor force FAI PM2.5 Output
Super efficiency Share of the tertiary DEA model industry puts a positive impact on city's environmental efficiency. Adjustment and upgrade of industrial structure would help to improve environmental efficiency in a city.
GDP (Fan et al. 2017)
To evaluate eco- Inputs efficiency of Land industrial parks in Energy China Water Outputs Industrial value added
DEA-CCR, BCC
DEA- Industrial value added per capita, industrial structure, policy and scale are the most important factors for eco-efficiency.
ACCEPTED MANUSCRIPT Wastewater Solid waste COD SO2
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4. Materials and methods 4.1. A Note on the DEA approach proposed by Salahi et al. (2016)
Data envelopment analysis (DEA) is a mathematical programming methodology, using common inputs and outputs as factors to obtain the relative efficiencies of DMUs (Tavana et
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al. 2018). The original DEA model, also named as the CCR model, proposed by Charnes et al.
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(1978), computes the relative efficiency of a DMU by maximizing the ratio of its total weighted outputs to its total weighted inputs subject to the condition that the output to input ratio of every DMU be less than or equal to unity.
Throughout the literature, common weight DEA-based models have been proposed in order to
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avoid the shortcomings of classical DEA models that do not provide a common evaluation for all DMUs, and require subjective assessment to determine input and output weights. Hence,
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common weight DEA-based models improve the discriminating power that restricts the selection of input and output weights in favour of respective DMUs (Karsak & Ahiska 2005).
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Minimax and minsum efficiency measures do not give favorable consideration to the evaluated DMU unlike the traditional DEA model. Minimax efficiency is to minimize the maximum deviation from efficiency, whereas minsum efficiency aims to minimize the total deviation from efficiency (Charnes et al. 1978).
Salahi et al. (2016) proposed two DEA-based formulations that can be employed in the presence of interval data. First, they introduced the following programming model to obtain the efficiency score of each DMU.
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max ∑ y r0 u r r =1
subject to
(1)
s
m
r =1
i =1
∑ y rj u r − ∑ xij vi ≤ 0,
∀j,
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m
∑ x i0 v i ≤ 1,
i =1 m
∑ xi0 v i ≥ 1,
ur , vi ≥ ε ,
∀r , i.
and
xij ∈[ x ij , xij ] denote the interval output and interval input data,
respectively.
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y rj ∈[ y rj , y rj ]
where
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i =1
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Salahi et al. (2016) also proposed the following common weight DEA-based model.
n m s min ∑ θ j ∑ xij vi − ∑ y rj u r j =1 i =1 r =1
s
m
r =1
i =1
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subject to
∀j ,
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∑ y rj u r − ∑ x ij vi ≤ 0,
ur , vi ≥ ε ,
∀r , i.
where θj refers to the objective function value of the formulation (1).
Finally, efficiency scores of DMUs based on common weights are calculated by
(2)
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∑ u r* y rj
θ 'j = r m=1
(3)
∑ vi* xij
i =1
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In order to illustrate the application of the proposed method, Salahi et al. (2016) used 17 forest districts data set, which is widely used in DEA literature. Although the data set includes crisp numbers, Salahi et al. (2016) convert it into interval data by assuming
[
where
[
]
[ y rj , y rj ] = y rj − 1, y rj + 1
and
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xij ∈[ x ij , xij ]
that y rj ∈[ y rj , y rj ] ,
]
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[ x ij , x ij ] = x ij − 1, x ij + 1 .
The efficiency scores obtained by employing formulation (1) are shown in Table 3. Although these scores are greater than 1, they are considered as 1 by Salahi et al. (2016). In the first constraint of the formulation (1), they consider that the difference between the weighted sum
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of lower bound of the outputs and the weighted sum of upper bound of the inputs should be lower than zero. However, the difference between the weighted sum of upper bound of the
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outputs and the weighted sum of lower bound of the inputs should be lower than zero. Otherwise, the obtained efficiency scores will excess unity, which contradicts principal notion
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of DEA. CCR model computes the relative efficiency of a DMU by maximizing the ratio of its total weighted outputs to its total weighted inputs subject to the condition that the output to input ratio of every DMU be less than or equal to unity. The first constraint in formulation (1) violates this condition of CCR, thus efficiency scores that are greater than unity can be obtained.
The improved model is given in the following formulation.
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max ∑ y r0 u r r =1
subject to
(4)
s
m
r =1
i =1
∑ y rj u r − ∑ x ij vi ≤ 0,
∀j,
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m
∑ x i0 v i ≤ 1,
i =1 m
∑ xi0 v i ≥ 1, ∀r , i.
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ur , vi ≥ ε ,
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i =1
The efficiency scores and ranking results of 17 forest districts obtained from the improved model is given in Table 3. The improved model yields the maximum efficiency score as 1,
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which supports the principal of DEA.
Table 3. Comparative results for 17 forest districts (ϵ = 0.000001)
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Efficiency scores obtained in Salahi et al. (2016) 1 1 1 1 1 1 1 1 1 0.9713 0.9605 0.8823 0.8216 0.8117 0.7805 0.7690 0.7415
Efficiency scores employing Formulation (1)
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DMU (j)
1.336966 1.039219 1.175585 1.238455 1.108511 1.307246 1.370864 1.045104 1.105414 0.971207 0.959503 0.881212 0.821111 0.811625 0.790047 0.768856 0.739964
Efficiency score Formulation (4)
Ranking results of Formulation (4)
1 1 1 1 1 1 1 1 1 0.943626 0.930861 0.825723 0.796455 0.783063 0.762876 0.746197 0.682959
1 1 1 1 1 1 1 1 1 10 11 12 13 14 15 16 17
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4.2. OIZ efficiency evaluation by using the proposed method
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This section illustrates the application of the proposed methodology conducting a case study for evaluating OIZs located in the cities under the responsibility of Eastern Black Sea Project Regional Development Administration. The numerical illustration involves 10 OIZs with
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“total industrial parcel area (Ha)”, and “total number of parcels” as inputs, “total number of parcels in production”, “employment”, and “budget for research & development (TRY)” as
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outputs. For this study two different data sources are used. One of these sources is the data of Directorate General for Industrial Zones. This directorate general is a department of Ministry of Science, Industry and Technology and it is responsible for industrial zones. The other source is Enterprise Information System, which is one of the important information systems of Ministry of Science, Industry and Technology. The most recent data in this information
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system related to OIZs is for the year 2015, hence the study is performed for 2015. Input and
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output data regarding OIZs are given Table 4.
Table 4. Input and output data regarding OIZs
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Output3 DMU Input1 Input2 Output1 Output2 (thousands of TRY) (j) 203 127 6 41 0 Bayburt [33,35] 37 21 729 0 Giresun 24 30 29 2824 0 Ordu-Fatsa 96 105 99 5115 [980,990] Samsun-Merkez [122,123] 145 19 240 [925,930] Samsun-Bafra [76,79] 61 23 614 [760,780] Samsun-Kavak 26 24 12 168 0 Samsun-Gıda İhtisas 75 104 12 1575 0 Tokat-Erbaa [72,73] 87 78 4447 [420,430] Trabzon-Arsin [20,22] 35 16 255 0 Trabzon-Beşikdüzü
ACCEPTED MANUSCRIPT 5. Results First, formulation (4) is solved in order to determine the efficiency score and rank of each DMU as given in Table 5. Ordu-Fatsa, Samsun-Merkez and Samsun-Kavak are determined as
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efficient DMUs.
Table 5. Efficiencies and rankings of OIZs
Rank 10 7 1 1 5 1 8 9 4 6
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Efficiency score obtained by Formulation (4) 0.043391 0.573959 1 1 0.692720 1 0.507201 0.178410 0.969558 0.648972
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DMU (j) Bayburt Giresun Ordu-Fatsa Samsun-Merkez Samsun-Bafra Samsun-Kavak Samsun-Gıda İhtisas Tokat-Erbaa Trabzon-Arsin Trabzon-Beşikdüzü
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Second, formulation (2) is solved for obtaining the common set of weights of inputs and outputs as listed in Table 6. Subsequently, formulation (3) is employed to achieve the most efficient DMU with regard to the efficiency scores, and final ranking results as shown in
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Ordu-Fatsa.
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Table 7. The common weight DEA-based model results in one single efficient OIZ that is
Table 6. Input and output weights V1 V2 U1 U2 U3
Weight 0.000010 0.000943 0.000010 0.000010 0.000048
ACCEPTED MANUSCRIPT Table 7. Final ranking results with respect to efficiency scores of OIZs Rank 10 6 1 2 5 4 9 7 3 8
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Efficiency score obtained by Formulation (3) 0.003859 0.212881 1 0.994449 0.341476 0.743250 0.078630 0.160592 0.793200 0.081590
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DMU (j) Bayburt Giresun Ordu-Fatsa Samsun-Merkez Samsun-Bafra Samsun-Kavak Samsun-Gıda İhtisas Tokat-Erbaa Trabzon-Arsin Trabzon-Beşikdüzü
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6. Discussion
OIZs are started to be established in the planned development period in Turkey. OIZ applications, which aim to provide industrial development and balanced regional development, to solve environmental problems by arranging relations between urbanization and industrialization, to utilize lower cost infrastructure services and to make production
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easier and cheaper, have reached to 55 years of experience (http://anahtar.sanayi.gov.tr). However, as a result of these 55 years of experience, when the OIZs are examined, it is seen
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that the occupancy rate and the passing rate of production are not sufficient.
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This paper aims to determine the most efficient OIZ that performs in the Eastern Black Sea region of Turkey by improving the DEA-based approach developed by Salahi et al. (2016). They proposed a DEA-based mathematical programming model, which can be employed in the presence of interval data. However, a limitation of their model is that the efficiency scores are greater than 1 that is impossible in DEA since the weighted sum of outputs cannot be greater than the weighted sum of inputs. In this work, this shortcoming is corrected by modifying the related constraint, the best OIZ alternative is indicated by solving a common weight DEA-based mathematical programming model. Firstly, formulation (4) is solved to
ACCEPTED MANUSCRIPT obtain the efficiency score and rank of each DMU. Ordu-Fatsa, Samsun-Merkez and SamsunKavak are indicated as efficient DMUs. Secondly, formulation (2) is solved in order to determine the common set of weights of inputs and outputs. Subsequently, formulation (3) is employed to achieve the most efficient DMU according to the efficiency scores, and final
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ranking results are obtained. The common weight DEA-based model results in one single efficient OIZ that is Ordu-Fatsa.
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Managerial implications of the proposed approach can be summarized as follows. DEA provides significant information that can be utilized for managerial purposes in decision
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making. The developed approach in this paper becomes practical tool for improving efficiency in industrialization by providing a performance assessment of OIZs, which are evaluated by taking into account two inputs as “total industrial parcel area”, and “total number of parcels”, and three outputs named “total number of parcels in production”, “employment”,
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and “budget for research & development”. According to the basic notion of DEA, inputs are to be minimized whereas the maximization of the outputs is aimed. Hereby, efficient working of OIZs is intended by minimizing total industrial parcel area and total number of parcels,
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however, maximization of total number of parcels in production increases the volume and sustainability of the production in the whole country. On the other hand, the proposed model
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supports employment opportunity by considering “employment” as an output to be maximized, and research & development by allocating the maximum budget. To conclude, the developed approach provides an efficiency analysis of OIZs, and ranks them with regard to their efficiency scores.
ACCEPTED MANUSCRIPT 7. Conclusions In this study, the best performing OIZ alternative, which performs in Eastern Black Sea region of Turkey, is identified based on the approach proposed by Salahi et al. (2016). A constraint in Salahi et al.’s model is reformulated due to an observed fault. Albeit the ratio of
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the weighted sum of the upper bounds of the outputs to the weighted sum of the lower bounds of the inputs must be smaller than or equal to one, their model considered that the weighted sum of the lower bounds of the outputs to the weighted sum of the upper bounds of the inputs
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must be smaller than or equal to one. Thus, in this study the related constraint of the Salahi et
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al.’s model is reorganized.
The data set used in Salahi et al. (2016) is resolved both using their model and improved model, and the results are compared. Moreover, a real world case study to determine the relative efficiencies of OIZs located in the Eastern Black Sea Region of Turkey, is conducted.
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Future research may focus on incorporating fuzzy data into the decision framework as well as
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Karsak EE, Ahiska SS (2005) Pratical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. International Journal of Production Research 43(8):1537-1554.
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Khodakarami M, Shabani A, Saen, RF (2014) A new look at measuring sustainability of industrial parks: a two-stage data envelopment analysis approach. Clean Technology
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Environment 16(8):1577-1596. Salahi M, Torabi N, Amiri A (2016) An optimistic robust optimization approach to
common set of weights in DEA. Measurement 93:67-73. Tavana M, Ebrahimnejad A, Santos-Arteaga FJ, Mansourzadeh SM , Matin RK (2018) A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia. Socio-Economic Planning Sciences 61:70-89.
ACCEPTED MANUSCRIPT Türkiye’de
Organize
Sanayi
Bölgeleri
Deneyimi
(2017).
http://anahtar.sanayi.gov.tr/tr/news/turkiyede-organize-sanayi-bolgeleri-deneyimi/63. Yenı̇ lmez F, Girginer N (2012) Assessing export performance of textile companies in Eskisehir Organized Industrial Zone by use of data envelopment analysis (DEA). Tekstil ve
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Konfeksiyon 22(1):12-17.
http://www.sanayi.gov.tr/DokumanGetHandler.ashx?dokumanId=93ccb086-48ae-
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4300-984f-5a799461ac4e
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Highlights
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•
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A DEA-based MCDM model is presented to provide performance assessment of OIZs located in Eastern Black Sea Region of Turkey. Imprecise DEA-based mathematical programming model developed by Salahi et al. (2016) is improved by modifying a constraint. The proposed approach, which generates common set of weights, can be employed in the presence of crisp and interval data. Two numerical illustrations are provided in order to demonstrate the robustness of the application. The first example provides a comparative analysis, whereas the second includes a real world application in Turkey.
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ACCEPTED MANUSCRIPT Mehtap Dursun is an assistant professor of Industrial Engineering at Galatasaray University, Turkey. She holds BS, MS, and PhD degrees in Industrial Engineering from Galatasaray University. Her areas of interest include quality function deployment, fuzzy optimization, and multi-criteria decision making with special focus on waste management, personnel selection, and supplier selection. She has coauthored articles that appeared in Expert Systems with Applications, Resources Conservation and Recycling, International Journal of Production Research, Applied Mathematical Modelling, and Kybernetes.
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Nazli Goker is a research assistant of Industrial Engineering at Galatasaray University, Turkey. She holds BS and MS degrees in Industrial Engineering from Galatasaray University. Her areas of interest include DEA-based models and multi-criteria decision making with special focus on performance management. She has co-authored article that appeared in Applied Soft Computing and Kybernetes.
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Burcu Deniz Tülek held MS degree in Industrial Engineering from Galatasaray University.
S0038-0121(18)30142-3
DOI:
https://doi.org/10.1016/j.seps.2018.10.010
Reference:
SEPS 659
To appear in:
Socio-Economic Planning Sciences
Received Date: 7 May 2018 Revised Date:
23 October 2018
Accepted Date: 28 October 2018
Please cite this article as: Dursun M, Goker N, Tulek BD, Efficiency analysis of organized industrial zones in Eastern Black Sea Region of Turkey, Socio-Economic Planning Sciences (2018), doi: https:// doi.org/10.1016/j.seps.2018.10.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Efficiency Analysis of Organized Industrial Zones in Eastern Black Sea Region of Turkey Mehtap Dursun*, Nazli Goker, Burcu Deniz Tulek Industrial Engineering Department, Galatasaray University, Ortakoy, Istanbul 34349, Turkey
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E-mail addresses: [email protected]; [email protected]; [email protected]
*
Corresponding author: Mehtap Dursun, Galatasaray University, [email protected]
ACCEPTED MANUSCRIPT Efficiency Analysis of Organized Industrial Zones in Eastern Black Sea Region of Turkey
Abstract
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Organized Industrial Zones (OIZs) are the production areas of goods and services that are established to provide planned industrialization and planned urbanization by structuring the industry in suitable areas, to prevent environmental problems, and to provide efficient use of
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resources. The aim of this study is to propose a decision-making model based on data envelopment analysis (DEA) to evaluate the relative efficiencies of OIZs located in the
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Eastern Black Sea Region of Turkey. First, the efficiency score of each alternative is determined by employing a DEA formulation with interval data. Second, a common weight DEA-based formulation is applied in order to obtain common set of weights and provide ranking results with an improved discriminating power in imprecise nature. In this study, the
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best performing OIZ alternative, which performs in Eastern Black Sea region of Turkey, is identified based on the approach proposed by Salahi et al. (2016). The DEA-based model developed by Salahi et al. (2016) is improved by modifying a constraint. In order to
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demonstrate the robustness of the application, two numerical illustrations are given. The first example compares the results obtained by the formulation addressed in Salahi et al. (2016)
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and the improved model; while second numerical illustration provides a case study conducted in Eastern Black Sea region of Turkey. Keywords: Data envelopment analysis, efficiency analysis, imprecise data, organized industrial zones
ACCEPTED MANUSCRIPT 1. Introduction
Organized Industrial Zones (OIZs) are important structures of the industry and they began to be established and supported by the Turkish State since 1960s. At the present time there are
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one or more OIZs in all cities of Turkey. The low occupancy rates of these OIZs make the question of whether the OIZs work efficiently. Although the need to evaluate the efficiencies of Turkey’s OIZs is stated on many sources, the studies related on this area are quite few and
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insufficient.
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The aim of this study is to propose a decision-making model based on data envelopment analysis to evaluate the relative efficiencies of OIZs located in the Eastern Black Sea Region of Turkey. The application procedure is based on Salahi et al.’s approach. A limitation in a constraint of the model proposed by Salahi et al. (2016) is detected and this constraint is
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reorganized. Salahi et al. (2016) suppose that the weighted sum of the lower bounds of the outputs is smaller than the weighted sum of the upper bounds of the inputs, however, this assumption yields efficiency scores that are greater than one, which contradicts principal
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notion of DEA. This contradiction is eradicated by reformulating the related constraint. The results of the formulation developed by Salahi et al. (2016) and the improved model are
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compared, and the efficiency scores of OIZ alternatives are identified via solving the improved model. The final efficiency values and ranking results are obtained by solving a common weight DEA-based programming model proposed by Salahi et al. (2016).
The rest of the paper is organized as follows. The following section presents OIZ public policy in Turkey. Section 3 gives a brief literature review on the use of DEA methods to evaluate the industrial zones’ or industrial zone’s firms’ efficiency. In Section 4, materials and
ACCEPTED MANUSCRIPT methods are presented. Results and discussions are provided in Section 5 and Section 6, respectively. Finally, concluding remarks and directions for future research are given in the
2. Organized industrial zone public policy in Turkey
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last section.
Organized industrial zones are structures that provide collective opportunities to the firms and
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aim to reduce the costs and facilitate the business of the companies with the special services they provide and thus contribute to the strengthening of the industry. These structures are
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supported by the government and investors are encouraged to prefer organized industrial zones. One of the objectives of the organized industrial zones is to encourage the backward regions without creating regional imbalances and to attract the industrial investments to these regions. For this reason, it is important to evaluate the status of organized industrial zones in
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regions with low level of development.
Organized Industrial Zones are the production areas of goods and services that;
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• are established to provide planned industrialization and planned urbanization by structuring the industry in suitable areas, to prevent environmental problems, to
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provide efficient use of resources,
• are equipped with necessary administrative, social and technical infrastructural areas and small manufacturing, repair, trade, education and health fields, technology development zones, and operated according to the provisions of “Organized Industrial Zones Law”.
ACCEPTED MANUSCRIPT Industrialization efforts in Turkey increased with the establishment of Republic and were seen as the base of economic development and newly acquired political independence. Industrialization was left to private sector initiatives in the first years of the Republic, but due to lack of financial power and lack of experience, the private sector could not fulfill this task
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as expected. Following the first efforts at industrialization, industrial infrastructure work was started within the frame of the "I. Five-Year Industrial Plan", which had been put into effect in 1931. And in the period of planned development, which started in 1960, it was clearly
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stated that the industry is the "locomotive" sector.
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Organized Industrial Zones and Small Industrial Sites have been started to be established by the government in 1960s for planned industrialization and planned urbanization (Cansiz 2010). In Turkey, OIZ implementations were first started with the establishment of an OIZ in Bursa in 1962.
In order to reduce regional development disparities and to provide regional
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development “Eastern Anatolia Project Regional Development Administration”, “Eastern Black Sea Project Regional Development Administration” and “Konya Plains Project Regional Development Administration” were established. The development graphs of
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1 and Figure 2.
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completed OIZs (as number and area) in Turkey over the years since 1990 are given in Figure
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Figure 1. Completed OIZs over the years on number basis (http://www.sanayi.gov.tr)
Figure 2. Completed OIZs over the years on hectare basis (http://www.sanayi.gov.tr)
All of the OIZs’ monitoring and evaluation operations are carried out by Ministry of Science, Industry and Technology in Turkey.
According to Ministry of Science, Industry and
Technology’s 01.03.2017 dated data in 297 OIZs and in 49.877 industrial parcels have been started production and 1.658.835 employments are provided. However, in existing OIZs there are also idle parcels except the parcels in production, and in some OIZs the ratio of idle
ACCEPTED MANUSCRIPT parcels to total parcels is quite high. Total zone size, number of parcels, employment and occupancy rates for the OIZs in Turkey are given in Table 1. Table 1. Turkey’s OIZs’ total data 89.160
Industrial Parcel Size (Ha)
51.126
Number of Parcels (Zoning)
75.734
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Zone Size (Ha)
Number of Parcels (Zone)
71.263
Total Number of Parcels
77.792
Size of Assigned Parcels (Ha) Number of Idle Parcels
58.144
39.771
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Number of Assigned Parcels
13.119 11.355
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Size of Idle Parcels (Ha) Number of Parcels in Production
49.877
Number of Parcels in Construction
3.414
Number of Parcels in Project
4.853
Employment Estimated Employment
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Occupancy Rate (Number of Assigned Parcels / Number of Parcels (Zone))
Rate of Parcels in Production (Number of Parcels in
2.409.323 68%
43%
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Production / Number of Parcels (Zone))
1.658.835
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As seen in Table 1, the average occupancy rate (Number of Assigned Parcels/ Number of Parcels) of the Turkey’s OIZs is 68% and cannot be said to be a high rate. On the other hand, the rate of parcels in production (Number of Parcels in Production/Number of Parcels) is lower and only 43%. When these rates are considered, it should be questioned whether the OIZs reach the desired efficiency.
ACCEPTED MANUSCRIPT 3. Literature review
Organized Industrial Zones are the production areas of goods and services that are established to provide planned industrialization and planned urbanization by structuring the industry in
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suitable areas, to prevent environmental problems and to provide efficient use of resources. Organized industrial zones allocate private sector investments to specific regions in order to balance regional development. Moreover, they meet land requirements of developing
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industries and support industrial production according to a specific program, hence they create an external economy. Besides, organized industrial zones contribute to national development
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goals by encouraging underdeveloped regions to where they invest for obtaining regional equality. They become instruments of development by providing spatial planning, manage and activate the efficiency of industrial production of underdeveloped regions. Thus, effective public policies should be implemented on OIZs. Recently, OIZs with low occupancy rates,
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which cannot provide the expected industrial development, are established due to the wrong investment decisions that have been made in order to eliminate regional inequalities, prevention of uneven growth in the country and long-term economic efficiency. However, it is
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necessary to take the right decisions to obtain the expected benefits from OIZs.
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Many governments support the implementation of OIZs due to their economic benefits. However, the low occupancy rates of these OIZs make the question of whether the OIZs work efficiently. Although the need to evaluate the efficiencies of OIZs is stated on many sources, the studies related on this area are quite few and insufficient. This section summarizes some significant studies about the use of DEA methods to evaluate the industrial zones’ or industrial zone’s firms’ efficiency.
ACCEPTED MANUSCRIPT Most of the studies deal with environmental impact. Hu et al. (2009) applied DEA to analyze the performance of 57 industrial parks in Taiwan from 2000 to 2004. DEA approach was used to compute the overall technical efficiency (OTE), pure technical efficiency (PTE), and scale efficiency (SE) scores. In the study the discrepancy efficiency was compared and the
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benchmark in all industrial parks was listed. Moreover, this paper intended to understand the directions of area economic development by analyzing the influence of environmental factors toward the efficiency of Taiwan’s industrial parks. Hu et al. (2010) used the four-stage DEA
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proposed by Fried et al. (1999) to evaluate the performances of science and technology industrial parks (STIPs). The data related to 53 China STIPs from 2004 to 2006 were The four-stage DEA was implemented by taking environmental factors into
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regarded.
consideration besides input and output variables and to provide an accurate assessment of the STIPs’ efficiencies. Khodakarami et al. (2014) proposed a gradual efficiency improvement DEA model and performed an application of the model using 31 Iranian industrial parks data.
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It was aimed to analyze the sustainability of industrial parks in this study. Environmental performance was used to determine follower and pioneer DMUs and it is tried to direct the follower DMUs toward pioneer DMUs to gradually increase their performance and plan how
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to achieve this improvement. Chen et al. (2017) evaluated environmental efficiency of 11 provinces and 131 cities in the Yangtze River Economic Zone in China proposing an
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approach based on super efficiency DEA and Malmquist index methods. Fan et al. (2017) assessed eco-efficiencies of 40 industrial parks in China by employing DEA methodology.
The studies that cope with macroeconomic results are as follows. Yenilmez and Girginer (2012) examined the efficiency of exporter textile firms’ in Eskisehir Organized Industrial Zone by implementing DEA which aims output maximization. 5 exporter firms’ input and output data for 2008 and 2009 was used to measure of the relative efficiencies of the firms.
ACCEPTED MANUSCRIPT Izadikhah and Farzipoor Saen (2015) proposed a new DEA approach to rank all efficient DMUs. The average of inefficient DMUs was defined as the virtual DMU. To overcome the drawbacks of the similar developed models, which cannot differentiate the efficient DMUs, the influence of efficient DMUs on both virtual DMU and other efficient DMUs were
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considered in the proposed model. Developed new approach was applied to the 17 industrial parks in Iran.
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The inputs and outputs, and also the employed model and obtained results of these studies are
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indicated in Table 2.
Table 2. Studies used in the literature review Ref.
Aim of the Study
Criteria Inputs
Capital stock
Number of workers
Results
CCR-DEA and BCC- Product-type and DEA models transportation are the two key factors of industrial park efficiency.
Number of firms
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(Hu et al. 2009)
To measure the efficiency of industrial parks in Taiwan
Model
Area of the firms
Utility maintenance fee Output
Operating Value
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Inputs
(Hu et al. 2010)
To investigate the efficiency of STIPs in China
(Yenilmez and Girginer 2012)
To analyze the efficiency of exporter firms in Eskisehir OIZ
Input-oriented DEA model
Number of firms set up in STIP Number of employees work in STIP Percentage of employees graduated from university to total employees Expenditure in R&D of STIP Percentage of science and technology personnel to total employees Outputs Technical and patent revenue of STIP Products sales revenue of STIP Commodity sales revenue of STIP CCR Inputs Production amount Number of employees
The environmental factors do affect the efficiency of the STIPs, especially in the west area.
Inefficient companies should decrease their production quantities whereas they have to
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Net assets of the firms Output Export values
Material cost Labor cost Investment Area of operation Intermediate Output/Inputs (Khodakarami et al. 2014)
To analyze the sustainability of industrial parks in Iran
Created companies Productions Environmental projects Outputs
Revenue
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Number of graduates
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Energy cost
2 stage DEA based Assessing sustainable network model performance of industrial parks and planning to improve performance are essential activities.
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Inputs
Number of active companies Effluent
CO2 emission
Welfare services Inputs
Number of workers
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Population Land area
(Izadikhah and Saen 2015)
To measure the efficiency of industrial parks in Iran
Working capital
A novel DEA-based Finds the efficiency technique for fully scores of 17 Iranian ranking all DMUs industrial parks based on changing reference set using a single virtual inefficient DMU.
Outputs
Number of signed contracts
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Volume of investment Number of created jobs Total revenue
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To assess environmental efficiency of 11 provinces and 131 cities in the Yangtze River Economic Zone in China
(Chen et al. 2017)
Inputs Labor force FAI PM2.5 Output
Super efficiency Share of the tertiary DEA model industry puts a positive impact on city's environmental efficiency. Adjustment and upgrade of industrial structure would help to improve environmental efficiency in a city.
GDP (Fan et al. 2017)
To evaluate eco- Inputs efficiency of Land industrial parks in Energy China Water Outputs Industrial value added
DEA-CCR, BCC
DEA- Industrial value added per capita, industrial structure, policy and scale are the most important factors for eco-efficiency.
ACCEPTED MANUSCRIPT Wastewater Solid waste COD SO2
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4. Materials and methods 4.1. A Note on the DEA approach proposed by Salahi et al. (2016)
Data envelopment analysis (DEA) is a mathematical programming methodology, using common inputs and outputs as factors to obtain the relative efficiencies of DMUs (Tavana et
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al. 2018). The original DEA model, also named as the CCR model, proposed by Charnes et al.
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(1978), computes the relative efficiency of a DMU by maximizing the ratio of its total weighted outputs to its total weighted inputs subject to the condition that the output to input ratio of every DMU be less than or equal to unity.
Throughout the literature, common weight DEA-based models have been proposed in order to
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avoid the shortcomings of classical DEA models that do not provide a common evaluation for all DMUs, and require subjective assessment to determine input and output weights. Hence,
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common weight DEA-based models improve the discriminating power that restricts the selection of input and output weights in favour of respective DMUs (Karsak & Ahiska 2005).
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Minimax and minsum efficiency measures do not give favorable consideration to the evaluated DMU unlike the traditional DEA model. Minimax efficiency is to minimize the maximum deviation from efficiency, whereas minsum efficiency aims to minimize the total deviation from efficiency (Charnes et al. 1978).
Salahi et al. (2016) proposed two DEA-based formulations that can be employed in the presence of interval data. First, they introduced the following programming model to obtain the efficiency score of each DMU.
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max ∑ y r0 u r r =1
subject to
(1)
s
m
r =1
i =1
∑ y rj u r − ∑ xij vi ≤ 0,
∀j,
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m
∑ x i0 v i ≤ 1,
i =1 m
∑ xi0 v i ≥ 1,
ur , vi ≥ ε ,
∀r , i.
and
xij ∈[ x ij , xij ] denote the interval output and interval input data,
respectively.
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y rj ∈[ y rj , y rj ]
where
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i =1
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Salahi et al. (2016) also proposed the following common weight DEA-based model.
n m s min ∑ θ j ∑ xij vi − ∑ y rj u r j =1 i =1 r =1
s
m
r =1
i =1
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subject to
∀j ,
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∑ y rj u r − ∑ x ij vi ≤ 0,
ur , vi ≥ ε ,
∀r , i.
where θj refers to the objective function value of the formulation (1).
Finally, efficiency scores of DMUs based on common weights are calculated by
(2)
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∑ u r* y rj
θ 'j = r m=1
(3)
∑ vi* xij
i =1
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In order to illustrate the application of the proposed method, Salahi et al. (2016) used 17 forest districts data set, which is widely used in DEA literature. Although the data set includes crisp numbers, Salahi et al. (2016) convert it into interval data by assuming
[
where
[
]
[ y rj , y rj ] = y rj − 1, y rj + 1
and
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xij ∈[ x ij , xij ]
that y rj ∈[ y rj , y rj ] ,
]
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[ x ij , x ij ] = x ij − 1, x ij + 1 .
The efficiency scores obtained by employing formulation (1) are shown in Table 3. Although these scores are greater than 1, they are considered as 1 by Salahi et al. (2016). In the first constraint of the formulation (1), they consider that the difference between the weighted sum
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of lower bound of the outputs and the weighted sum of upper bound of the inputs should be lower than zero. However, the difference between the weighted sum of upper bound of the
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outputs and the weighted sum of lower bound of the inputs should be lower than zero. Otherwise, the obtained efficiency scores will excess unity, which contradicts principal notion
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of DEA. CCR model computes the relative efficiency of a DMU by maximizing the ratio of its total weighted outputs to its total weighted inputs subject to the condition that the output to input ratio of every DMU be less than or equal to unity. The first constraint in formulation (1) violates this condition of CCR, thus efficiency scores that are greater than unity can be obtained.
The improved model is given in the following formulation.
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max ∑ y r0 u r r =1
subject to
(4)
s
m
r =1
i =1
∑ y rj u r − ∑ x ij vi ≤ 0,
∀j,
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m
∑ x i0 v i ≤ 1,
i =1 m
∑ xi0 v i ≥ 1, ∀r , i.
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ur , vi ≥ ε ,
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i =1
The efficiency scores and ranking results of 17 forest districts obtained from the improved model is given in Table 3. The improved model yields the maximum efficiency score as 1,
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which supports the principal of DEA.
Table 3. Comparative results for 17 forest districts (ϵ = 0.000001)
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Efficiency scores obtained in Salahi et al. (2016) 1 1 1 1 1 1 1 1 1 0.9713 0.9605 0.8823 0.8216 0.8117 0.7805 0.7690 0.7415
Efficiency scores employing Formulation (1)
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DMU (j)
1.336966 1.039219 1.175585 1.238455 1.108511 1.307246 1.370864 1.045104 1.105414 0.971207 0.959503 0.881212 0.821111 0.811625 0.790047 0.768856 0.739964
Efficiency score Formulation (4)
Ranking results of Formulation (4)
1 1 1 1 1 1 1 1 1 0.943626 0.930861 0.825723 0.796455 0.783063 0.762876 0.746197 0.682959
1 1 1 1 1 1 1 1 1 10 11 12 13 14 15 16 17
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4.2. OIZ efficiency evaluation by using the proposed method
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This section illustrates the application of the proposed methodology conducting a case study for evaluating OIZs located in the cities under the responsibility of Eastern Black Sea Project Regional Development Administration. The numerical illustration involves 10 OIZs with
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“total industrial parcel area (Ha)”, and “total number of parcels” as inputs, “total number of parcels in production”, “employment”, and “budget for research & development (TRY)” as
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outputs. For this study two different data sources are used. One of these sources is the data of Directorate General for Industrial Zones. This directorate general is a department of Ministry of Science, Industry and Technology and it is responsible for industrial zones. The other source is Enterprise Information System, which is one of the important information systems of Ministry of Science, Industry and Technology. The most recent data in this information
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system related to OIZs is for the year 2015, hence the study is performed for 2015. Input and
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output data regarding OIZs are given Table 4.
Table 4. Input and output data regarding OIZs
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Output3 DMU Input1 Input2 Output1 Output2 (thousands of TRY) (j) 203 127 6 41 0 Bayburt [33,35] 37 21 729 0 Giresun 24 30 29 2824 0 Ordu-Fatsa 96 105 99 5115 [980,990] Samsun-Merkez [122,123] 145 19 240 [925,930] Samsun-Bafra [76,79] 61 23 614 [760,780] Samsun-Kavak 26 24 12 168 0 Samsun-Gıda İhtisas 75 104 12 1575 0 Tokat-Erbaa [72,73] 87 78 4447 [420,430] Trabzon-Arsin [20,22] 35 16 255 0 Trabzon-Beşikdüzü
ACCEPTED MANUSCRIPT 5. Results First, formulation (4) is solved in order to determine the efficiency score and rank of each DMU as given in Table 5. Ordu-Fatsa, Samsun-Merkez and Samsun-Kavak are determined as
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efficient DMUs.
Table 5. Efficiencies and rankings of OIZs
Rank 10 7 1 1 5 1 8 9 4 6
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Efficiency score obtained by Formulation (4) 0.043391 0.573959 1 1 0.692720 1 0.507201 0.178410 0.969558 0.648972
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DMU (j) Bayburt Giresun Ordu-Fatsa Samsun-Merkez Samsun-Bafra Samsun-Kavak Samsun-Gıda İhtisas Tokat-Erbaa Trabzon-Arsin Trabzon-Beşikdüzü
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Second, formulation (2) is solved for obtaining the common set of weights of inputs and outputs as listed in Table 6. Subsequently, formulation (3) is employed to achieve the most efficient DMU with regard to the efficiency scores, and final ranking results as shown in
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Ordu-Fatsa.
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Table 7. The common weight DEA-based model results in one single efficient OIZ that is
Table 6. Input and output weights V1 V2 U1 U2 U3
Weight 0.000010 0.000943 0.000010 0.000010 0.000048
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Efficiency score obtained by Formulation (3) 0.003859 0.212881 1 0.994449 0.341476 0.743250 0.078630 0.160592 0.793200 0.081590
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DMU (j) Bayburt Giresun Ordu-Fatsa Samsun-Merkez Samsun-Bafra Samsun-Kavak Samsun-Gıda İhtisas Tokat-Erbaa Trabzon-Arsin Trabzon-Beşikdüzü
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6. Discussion
OIZs are started to be established in the planned development period in Turkey. OIZ applications, which aim to provide industrial development and balanced regional development, to solve environmental problems by arranging relations between urbanization and industrialization, to utilize lower cost infrastructure services and to make production
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easier and cheaper, have reached to 55 years of experience (http://anahtar.sanayi.gov.tr). However, as a result of these 55 years of experience, when the OIZs are examined, it is seen
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that the occupancy rate and the passing rate of production are not sufficient.
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This paper aims to determine the most efficient OIZ that performs in the Eastern Black Sea region of Turkey by improving the DEA-based approach developed by Salahi et al. (2016). They proposed a DEA-based mathematical programming model, which can be employed in the presence of interval data. However, a limitation of their model is that the efficiency scores are greater than 1 that is impossible in DEA since the weighted sum of outputs cannot be greater than the weighted sum of inputs. In this work, this shortcoming is corrected by modifying the related constraint, the best OIZ alternative is indicated by solving a common weight DEA-based mathematical programming model. Firstly, formulation (4) is solved to
ACCEPTED MANUSCRIPT obtain the efficiency score and rank of each DMU. Ordu-Fatsa, Samsun-Merkez and SamsunKavak are indicated as efficient DMUs. Secondly, formulation (2) is solved in order to determine the common set of weights of inputs and outputs. Subsequently, formulation (3) is employed to achieve the most efficient DMU according to the efficiency scores, and final
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ranking results are obtained. The common weight DEA-based model results in one single efficient OIZ that is Ordu-Fatsa.
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Managerial implications of the proposed approach can be summarized as follows. DEA provides significant information that can be utilized for managerial purposes in decision
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making. The developed approach in this paper becomes practical tool for improving efficiency in industrialization by providing a performance assessment of OIZs, which are evaluated by taking into account two inputs as “total industrial parcel area”, and “total number of parcels”, and three outputs named “total number of parcels in production”, “employment”,
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and “budget for research & development”. According to the basic notion of DEA, inputs are to be minimized whereas the maximization of the outputs is aimed. Hereby, efficient working of OIZs is intended by minimizing total industrial parcel area and total number of parcels,
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however, maximization of total number of parcels in production increases the volume and sustainability of the production in the whole country. On the other hand, the proposed model
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supports employment opportunity by considering “employment” as an output to be maximized, and research & development by allocating the maximum budget. To conclude, the developed approach provides an efficiency analysis of OIZs, and ranks them with regard to their efficiency scores.
ACCEPTED MANUSCRIPT 7. Conclusions In this study, the best performing OIZ alternative, which performs in Eastern Black Sea region of Turkey, is identified based on the approach proposed by Salahi et al. (2016). A constraint in Salahi et al.’s model is reformulated due to an observed fault. Albeit the ratio of
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the weighted sum of the upper bounds of the outputs to the weighted sum of the lower bounds of the inputs must be smaller than or equal to one, their model considered that the weighted sum of the lower bounds of the outputs to the weighted sum of the upper bounds of the inputs
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must be smaller than or equal to one. Thus, in this study the related constraint of the Salahi et
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The data set used in Salahi et al. (2016) is resolved both using their model and improved model, and the results are compared. Moreover, a real world case study to determine the relative efficiencies of OIZs located in the Eastern Black Sea Region of Turkey, is conducted.
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Future research may focus on incorporating fuzzy data into the decision framework as well as
References
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evaluating the efficiency of OIZs that are located in the other regions of Turkey.
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ACCEPTED MANUSCRIPT Fan Y, Bai BB, Qiao Q, Kang P, Zhang Y, Guo J (2017) Study on eco-efficiency of industrial parks in China based on data envelopment analysis. Journal of Environmental Management 192:107-115. Fried HO, Schmidt SS, Yaisawarng, S (1999) Incorporating the operating environment
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common set of weights in DEA. Measurement 93:67-73. Tavana M, Ebrahimnejad A, Santos-Arteaga FJ, Mansourzadeh SM , Matin RK (2018) A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia. Socio-Economic Planning Sciences 61:70-89.
ACCEPTED MANUSCRIPT Türkiye’de
Organize
Sanayi
Bölgeleri
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(2017).
http://anahtar.sanayi.gov.tr/tr/news/turkiyede-organize-sanayi-bolgeleri-deneyimi/63. Yenı̇ lmez F, Girginer N (2012) Assessing export performance of textile companies in Eskisehir Organized Industrial Zone by use of data envelopment analysis (DEA). Tekstil ve
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http://www.sanayi.gov.tr/DokumanGetHandler.ashx?dokumanId=93ccb086-48ae-
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Highlights
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A DEA-based MCDM model is presented to provide performance assessment of OIZs located in Eastern Black Sea Region of Turkey. Imprecise DEA-based mathematical programming model developed by Salahi et al. (2016) is improved by modifying a constraint. The proposed approach, which generates common set of weights, can be employed in the presence of crisp and interval data. Two numerical illustrations are provided in order to demonstrate the robustness of the application. The first example provides a comparative analysis, whereas the second includes a real world application in Turkey.
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ACCEPTED MANUSCRIPT Mehtap Dursun is an assistant professor of Industrial Engineering at Galatasaray University, Turkey. She holds BS, MS, and PhD degrees in Industrial Engineering from Galatasaray University. Her areas of interest include quality function deployment, fuzzy optimization, and multi-criteria decision making with special focus on waste management, personnel selection, and supplier selection. She has coauthored articles that appeared in Expert Systems with Applications, Resources Conservation and Recycling, International Journal of Production Research, Applied Mathematical Modelling, and Kybernetes.
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Nazli Goker is a research assistant of Industrial Engineering at Galatasaray University, Turkey. She holds BS and MS degrees in Industrial Engineering from Galatasaray University. Her areas of interest include DEA-based models and multi-criteria decision making with special focus on performance management. She has co-authored article that appeared in Applied Soft Computing and Kybernetes.
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Burcu Deniz Tülek held MS degree in Industrial Engineering from Galatasaray University.