sustainability

Article
Supply Chain Efficiency Measurement to Maintain
Sustainable Performance in the Automobile Industry
Illi Kim 1 and Changhee Kim 2, * ID

1 College of Business Administration, Seoul National University, Seoul 08826, Korea; fatumdeae@snu.ac.kr
2 College of Business Administration, Incheon National University, Incheon 22012, Korea
* Correspondence: ckim@inu.ac.kr; Tel.: +83-32-835-8734




Received: 11 July 2018; Accepted: 5 August 2018; Published: 10 August 2018


Abstract: The automobile industry is set to undergo a structural transformation in the progress toward
next-generation industries that involve autonomous vehicles and connected cars. Thus, supply chain
management has become increasingly important for corporate competitiveness. This study aims to
identify opportunities for improving supply chain performance by quantifying the impact of suppliers
on the supply chain. An analysis was conducted in two phases. First, the efficiency of 139 partners
that supply automobile components to the Hyundai Motor Company was measured using the
Charnes–Cooper–Rhodes model, while the efficiency of Hyundai Motor Company’s 540 supply
chains comprising partners, subsidiaries, and parent companies was measured using the network
epsilon-based measure model. Second, the relationship between the partner efficiency and the
supply chain efficiency was analyzed using the Mann–Whitney U test and the Tobit regression model.
The results showed that efficient operation of partners hampers the efficiency of the total supply
chain. Thus, there may be several partners that are not committed to quality improvement, while the
Hyundai Motor Company seeks to promote quality management through win–win cooperation with
partners. Consequently, automakers must review their partner management system, including their
performance measurement and incentive systems.

Keywords: automobile industry; efficiency analysis; supply chain management; supplier selection;
network DEA; epsilon-based measure

1. Introduction
The current automobile industry is undergoing structural changes because of its convergence
with cutting-edge information and communication technologies—such as artificial intelligence and the
Internet of Things, along with big data—in order to produce next-generation automobiles. To achieve
sustainable competitiveness and maximize operational efficiency, the importance of the supply
chain has been further emphasized [1]. The systematic management of the supply chain requires
activities such as demand forecasting, production planning and scheduling, procurement, inventory
management, and logistics to be managed at an integrated supply chain level, rather than an individual
company level [2].
Over the last three decades, studies on supply chain management have traditionally focused
on a cooperative supply chain and analyzed the effects of cooperation within the supply chain on
the performance improvement. Such research has covered transaction cost theory, resource-based
theory, knowledge-based theory, and game theory [1] for case studies on Toyota, Hewlett Packard
Enterprise, and Walmart among others [3–5]. In addition, studies on the establishment of an efficient
and sustainable supply chain have been actively conducted [6–9].
Most of the extant literature has examined the supply chain in its simplest form and identified the
relationship between the buyer–supplier partnership and the supply chain performance. Nevertheless,

Sustainability 2018, 10, 2852; doi:10.3390/su10082852 www.mdpi.com/journal/sustainability

Sustainability 2018, 10, 2852 2 of 16

they are limited in their evaluation of supply chain performance using the efficiency and effectiveness of
individual companies. The measurement of supply chain performance must be holistically conducted,
rather than being focused on the individual level. This is because in conditions where a conflict
of interests arises between supply chain players, an efficient operation for one player may lead to
an inefficient operation for another player in the supply chain. This would ultimately hamper the
efficiency of the entire supply chain [10]. Therefore, to assess the supply chain performance, the nature
of and interactions within the supply chain network all need to be taken into consideration in order to
adjust and integrate the performance of supply chain players [11].
The automobile industry in Korea has a top-down (vertical) structure, where automakers exercise
power over partners, which is unlike that in the U.S., where automobile suppliers have grown
independently [12]. Hyundai and Kia Motors occupied over 80% of the domestic automobile market,
and it leads to a heightened awareness that large conglomerates’ opportunistic practices for short-term
interests pose serious threats to the survival of small- and medium-sized enterprises (SMEs). As
an alternative to this status quo, policies on win–win cooperation that seek to promote mid- to
long-term (sustainable) relationship and mutual growth of automakers and partners have been put
forward [13].
The present study aims to empirically analyze the effects of improved competitiveness of the
partners (through win–win cooperation) on the efficiency of the total supply chain. The Hyundai
Motor Company has provided financial and technological assistance to its partners, leading them to
actively participate in the quality improvement process. However, without integrating the supply
chain, such policies are likely to cause inefficiency in the overall supply chain. We hypothesized that
an efficient partner with high profitability might maintain quality only to the minimum requirement,
and thereby disrupt the supply chain performance. This hypothesis was tested through a three-tier
supply chain of partners, subsidiaries, and parent companies in the automobile industry. The rest of
the paper is organized as follows. Section 2 examines the literature on supply chain management in
the automobile industry. Section 3 describes the data envelopment analysis (DEA) model that we build.
Section 4 presents data and criteria for variable selection, and summarizes the results from two DEA
models that we use to evaluate the efficiency of partners and supply chains, respectively. Section 5
applies the Mann–Whitney U test and the Tobit regression model to the DEA results, and discusses
the relationship between these two efficiency scores. Section 6 concludes the paper and suggests
future directions.

2. Literature Review
Owing to unstable demand and excessive supply, automakers have faced immense competitive
pressure. In light of the increasing need for sustainable supply chain management that allows
an optimized material flow, various studies have been conducted on performance evaluation and
benchmarking of supply chains [14].
The supply chain is a complex network in which multiple companies interact with one another
in a business process. The evaluation of its performance can be defined as a process that measures
its efficiency [15]. The most commonly used method to analyze supply chain efficiency is DEA,
a non-parametric approach that estimates the relative efficiency of decision-making units (DMU)
with multiple inputs and outputs [16]. DEA, unlike a typical supply chain optimization model,
has an advantage—it does not require unrealistic prior consumption for variables [17]. However,
traditional DEA models, such as Charnes–Cooper–Rhodes (CCR) and Banker–Charnes–Cooper (BCC)
models, treat the production process of the DMU as a black box and have been criticized for not clearly
identifying the relationship between inputs and outputs. To address this limitation, a network DEA
model was developed to divide the production process of the DMU into multiple processes between
the divisions and then calculate the efficiency of the entire networked system [18]. The network
DEA model can deal with processes in various forms, including serial, parallel, mixed, hierarchical,
and dynamic systems [19]. It can be also extended to hybrid models by combining it with other
Sustainability 2018, 10, 2852 3 of 16

decision-making methods, such as analytic hierarchy process (AHP), stochastic programming, goal
programming, and neural networks. Thus, the network DEA model is used in the banking, aviation,
transport, manufacturing, and sports industries; furthermore, the scope of its application continues to
gradually expand [20].
In the automobile industry, studies that utilize DEA for supply chain management have primarily
evaluated the efficiency of auto parts manufacturers in relation to supplier selection. Zeydan et al. [21]
used a fuzzy AHP on trunk panel manufacturers to obtain qualitative variables that were then
converted into quantitative variables. These were designated as the outputs of the DEA model to
measure the efficiency of suppliers and exclude inefficient suppliers. Ha and Krishnan [22] operated
a supplier portfolio by conducting a cluster analysis based on qualitative and quantitative factors
obtained from AHP, neural networks, and DEA in order to select competitive suppliers among
automatic transmission manufacturers. Çelebi and Bayraktar [23] employed neural networks to
process incomplete supplier data of a local auto assembly plant that imports components from
overseas suppliers to establish evaluation criteria shared by all the DMUs. They applied DEA to
form a partnership with suppliers classified as efficient DMUs to improve operational efficiency.
Several studies have identified the cause of the differences in efficiency of automobile suppliers.
Talluri et al. [24] estimated the efficiency of 150 primary suppliers for three major automakers in the
U.S.—GM, Ford, and Chrysler—and categorized them into three groups of high, medium, and low
according to their efficiency scores. Then, they utilized a Kruskal–Wallis test to detect between-group
differences in cost, quality, on-time delivery, flexibility, and innovation variables. The most efficient
and least efficient groups showed a significant difference only in cost, indicating that efficient suppliers
were successful in cost reduction. Manello et al. [25] examined changes in the total factor productivity
of numerous companies in the Italian automobile supply chain over a four-year period after the
financial crisis. They used a bootstrapped Malmquist index and reported that firms concentrating
on their core business were more efficient than the others. Moreover, in contrast with SMEs, large
conglomerates were located near the efficient frontier, which hindered them from benefiting from
catching-up effects (emulating other companies), and thus allowed productivity improvement only by
technological innovation.
Meanwhile, some studies have discussed a correlation between the supplier–automaker
relationship and the supply chain efficiency. Saranga [14] investigated the Indian automobile
industry; the author described a case in which a small-scale manufacturer at a low level of the
supply chain had to make advanced payments for raw materials and receive after-payment for
supplied components. Owing to this difficult financing environment, instead of using automated
equipment, the manufacturing process was undertaken manually, which caused inefficiency in the
operation of automobile suppliers. The study further suggested that, to ensure an efficient supply
chain, automakers at high levels of the supply chain must provide those suppliers with financial and
technological support, as well as long-term supply contracts. This would affect the cost reduction
and quality improvement of the automobile supply chain. Sadjadi and Bayati [26] applied game
theory to the relationship between raw material producers and auto parts manufacturers in a three-tier
supply chain (raw material producers, auto parts manufacturers, and automakers). They computed
supply chain efficiency in a cooperative game, where all suppliers made efforts to promote overall
efficiency, and then in a non-cooperative game, where a leader maximized its efficiency and a follower
made decisions sequentially, taking the efficiency of the leader as a fixed value (Stackelberg model).
The results showed that the optimal efficiency of the cooperative game was greater than or equivalent
to that of the non-cooperative game.
In supply chain management, decision-making by individual entities affects not only those
entities, but also their counterparts, which ultimately determines the efficiency of the total supply
chain. However, most previous studies on automobile supply chain have mainly focused on the
individual suppliers. In addition, many studies have examined the two-tier supply chain comprising
automobile suppliers and automakers. Few studies have considered a three-tier or higher supply
Sustainability 2018, 10, 2852 4 of 16

chain. Thus, this study sets a three-tier supply chain comprising partners, subsidiaries, and parent
companies. The individual and overall efficiencies of the supply chain are analyzed to verify the
impact of individual entities on the supply chain.

3. Methodology

3.1. CCR Model

DEA, introduced by Charnes et al. [27], is a linear programming approach that measures the
relative efficiency of the homogeneous DMU using the distance from DMU to an efficient frontier.
It establishes the efficient frontier, which is a combination of optimal inputs and outputs, based on the
observed data. The efficiency score of the DMU is defined as the ratio of a total weighted output to
a total weighted input. The weight of inputs and outputs is estimated as a value that maximizes the
efficiency score of the DMU for evaluation, under the constraint that the efficiency score of all DMUs is
less than or equivalent to 1.
DEA is generally separated into two models—an input-oriented model that minimizes the input
at a given output level and an output-oriented model that maximizes the output at a given input level.
When a supply contract is signed between automakers and partners, the unit price, quantity, quality
standards, and other details of the components to be produced by the partners are pre-determined.
Therefore, we measure the partner efficiency using an input-oriented model. The DMU of the CCR
model is a partner, and the efficiency score of an input-oriented CCR model with a certain number of
DMUs, n, is calculated as follows:

θ ∗ = min θ,
s.t. ∑nj=1 xij λ j + si− = θxio
(1)
∑nj=1 yrj λ j ≥ yro
λ j ≥ 0, si− ≥ 0 i = 1, . . . , m; r = 1, . . . , q; j = 1, . . . , n

where θ ∗ is the efficiency score of the DMU0 for evaluation; xij and yrj are the ith input (i = 1, . . . , m)
and the rth output (r = 1, . . . , q) of the jth DMU (j = 1, . . . , n), respectively; λ is the intensity vector
and s− is the input slacks; m and q are the number of inputs and outputs, respectively.
The efficiency score of the CCR model θ is computed by considering all inputs and outputs of
different divisions that exist in the DMUs. However, the CCR model presents a problem—as it regards
the production process within the DMU as a black box, it is inadequate to capture the internal activities
among divisions.

3.2. NEBM Model

The network DEA model, unlike traditional DEA models, measures the efficiency of the DMUs
by reflecting the production process of converting inputs into outputs in the network structure. In the
network DEA model, an output from a process that is used as an input for another process is referred
to as an intermediate measure; the overall efficiency is calculated through an optimization process [19].
DEA features two models—a radial model that assumes a proportionate change of inputs and
outputs and a non-radial model that assumes a non-proportionate change of inputs and outputs.
However, the CCR model, which is the most typical radial model, does not consider non-radial slacks,
while the slack-based measure (SBM) model, which is the most typical non-radial model, does not
consider radial slacks [28]. Therefore, Tone and Tsutsui [29] suggested an epsilon-based measure (EBM)
model that integrates both radial and non-radial approaches.
Tone and Tsutsui [30] developed a network slack-based measure (NSBM) model by extending the
SBM model to a network structure, while Tavana et al. [15] proposed a network epsilon-based measure
(NEBM) model which applied the EBM model to the network structure. The NEBM model considers
all the radial and non-radial slacks. Thus, it can reflect a complex nature of a multilayered supply
chain in which multiple divisions interact with one another. Moreover, as only the DMUs in which all
Sustainability 2018, 10, 2852 5 of 16

Sustainability 2018, 10, x FOR PEER REVIEW    5 of 16 
divisions are efficient achieve an NEBM efficiency score of 1, this model can effectively discriminate
has a strong discriminatory power between efficient and inefficient DMUs. Figure 1 summarizes the 
between efficient and inefficient DMUs. Figure 1 summarizes the categorization with respect to the
categorization with respect to the DEA approaches we utilize in this study. 
DEA approaches we utilize in this study.

 
Figure 1. Data envelopment analysis (DEA) method categorization. CCR: Charnes–Cooper–Rhodes;
Figure 1. Data envelopment analysis (DEA) method categorization. CCR: Charnes–Cooper–Rhodes; 
BCC: Banker–Charnes–Cooper; SBM: slack-based measure; EBM: epsilon-based measure.
BCC: Banker–Charnes–Cooper; SBM: slack‐based measure; EBM: epsilon‐based measure. 

According 
In the supplyto chain,
an  automobile  component 
the partners supply 
produce and contract, 
deliver when components
automobile partners  produce  and  deliver 
to the subsidiaries
automobile 
according components, 
to the Hyundai 
supply contract. Motors’ 
Then, the subsidiaries 
subsidiaries semi‐assemble 
semi-assemble the  components 
the components to manufactureto 
manufacture a module, while its parent companies assemble the modules into a finished automobile. 
a module, and the parent companies assemble the modules into a finished vehicle. As the supply
As  subsidiaries 
chain and  parent 
would maximize companies 
productivity with would  pursue 
the supplied an  efficient we
components, operation 
measure to 
themaximize  the 
supply chain
productivity with the supplied components, we measure the supply chain efficiency using an output‐
efficiency using an output-oriented model. The DMU of the NEBM model is a supply chain, and its
oriented model. The DMU of the NEBM model is a supply chain, and its structure is shown in Figure 
structure is described in Figure 2. The efficiency score of an output-oriented NEBM model, with n
2. The efficiency score of an output‐oriented NEBM model, with n DMUs consisting of k divisions, is 
DMUs consisting of k divisions, is calculated as follows:
calculated as follows: 
q wrh+ srh+
∗ 1/γ∗ = maxθ,λ,s+ ∑kh=1 Wh (θh + , eyh ∑r=
h
1 h ),
yro
s.t. , , ∑nj=∑1 xijh λhj ≤ xio h∑, i = 1, . . . , m ; h = 1, . . . , k
h

s. t. ∑ ∑nj=1 yrj h λ h − s h+ = θ y h , r = 1, . . . , q ; h = 1, . . . , k
, j r1, … , h ; ro
1, … , ,  h
(h,h0 ) (h,h0 )
∑nj=1 z f λhj = z f 0 0 (2)
      ∑ (h,h0 ) j ,
(h,h ) 1, … , ; 1, … , , 
(h,h0 ) 0 (h,h0 )
,
∑nj=1 z f 0 j λ, hj = z f 0 0 , f (h,h0 ) = 1, . . . , F(h,h0 ) , ∀(h, h0 )
      
∑ (h,h )  
(h,h )
, θh ≤ 1, h = 1,, . . . , k (2) 
, λhj ≥ 0, srh+ ,≥ 0, r = 1, . . . , qh ; h = 1, . . . , k; j = 1, . . . , n
      
∑ , , 1, … , , , ∀ , , 
, ,
where γ∗ is the efficiency score of the DMU0 for evaluation; xijh and yrj h are the ith input (i = 1, . . . , m )
h
1, 1, … , , 
and the rth output (r = 1, . . . , qh ) of division h (h = 1, . . . , k) within the jth supply chain (j = 1, . . . , n),
0, vector
respectively; λh is the intensity 1, … , and
; sh+1,is…the
, , output slacks corresponding to division h; mh and
(h,h0 )
qh are the number of inputs0,
and outputs
1, … , of 1, … ,h,. respectively; z f
; division is the intermediate measure
(h,h0 ) j
from division h to division h0 within the jth supply chain; and F(h,h0 ) is the number of intermediate
where ∗   is the efficiency score of the DMU 0 for evaluation;    and    are the ith input (i = 1, …, 
measures from division h and to division h0 .
) and the rth output (r = 1, …,  ) of division h (h = 1, …, k) within the jth supply chain (j = 1, …, 
n), respectively;    is the intensity variable and    is the output slacks in division h;    and    are 
,
the  number  of  inputs  and  outputs  of  division  h,  respectively;    is  an  intermediate  product 
,

transferred  from  division  h  to  division  h′  within  the  jth  supply  chain;  and  the  suffix  ,   is  the 
number of intermediate products between division h and division h′ ( , 1, … , , ). 
  is the weight of the rth output in division h that satisfies  ∑ 1.    is a parameter 
dependent on the degree of dispersion of the outputs in division h.    is the weight of division h 
 of partners, subsidiaries, and parent companies in a balanced manner. 
The first and second constraints are for the inputs and outputs of division h. The third constraint 
is  a  linking  constraint  for  intermediate  products  between  division  h  and  division  h′;  linking 
constraints include both free links, where the linking activities are freely determined, and fixed links, 
where  they  are  kept  unchanged  [30].  In  this  study,  as  the  divisions  of  the  supply  chain  are 
Sustainability 2018, 10, 2852
independently operated companies, fixed links were appropriate where all intermediate products are  6 of 16
determined outside the discretion of the managers of the companies. 

                         

                                   
, ,
           
, ,
Division  Division  …  Division  Division  …  Division 

1    2      h    h′      k 

                                   
                         

Figure 2. General structure of the supply chain. 
Figure 2. General structure of the supply chain.

The    value is derived from the dispersion of outputs—the greater the dispersion, the greater 
h+ h+ q
w the weight of the rth output in division h that satisfies ∑r=
r is  value. If the degree of dispersion between the outputs is very low, the 
the 
h
1. eyh is a parameter
1 wr =  value becomes 0, 
dependent on the degree of dispersion of the outputs in division h. Wh is the weight of division h
and the NEBM model changes into the network Charnes‐Cooper‐Rhodes (NCCR) model below. 
imposed by decision-makers.
∗ This study assigns Wh equally to take into account the significance of
, ∑ , 
partners, subsidiaries, and parent companies in a balanced manner.
The first and s. t. ∑ constraints are
second , for 1, … inputs
the , ; and1, outputs
… , ,  of division h, respectively. The third

          ∑are linking constraints

and fourth constraints , 1,
for … ,the; intermediate
1, … , ,  measures between division h and
0
division h ; they are classified into a free
, , link where the linking activities are freely determined and
       ∑  
a fixed link where the linking ,

activities , are kept unchanged [30]. In this study, as the divisions (3) 
of the supply chain are independently
, , operated companies, the fixed link is appropriate where all
       ∑ , , 1, … , , , ∀ , , 
intermediate measures are , determined outside , the discretion of managers in each company.
eyh is estimated from the 1, dispersion1, … , ,  of the outputs—the greater the dispersion, the greater the eyh
value. If the degree of dispersion between the outputs is very low, eyh becomes 0, and the NEBM model
0, 1, … , ; 1, … , . 
changes into the network Charnes-Cooper-Rhodes (NCCR) model below.
On  the  contrary,  if  the  degree  of  dispersion  between  the  outputs  is  very  high,  the    value 
1/θ ∗ = max + ∑
k
becomes 1, and the NEBM model changes into the NSBM model below [30]. 
Wh θh , θ,λ,s h =1
∗s.t. ∑nj=1 xijh λhj ≤ xio
h , i = 1, . . . , m ; h = 1, . . . , k
,  h
, ∑∑
n h h ∑ h+ h
j=1 yrj λ j − sr = θ h yro , r = 1, . . . , q h ; h = 1, . . . , k
(h,h0 ) (h,h0 )
s. t. ∑ ∑nj=1 z f λhj =1,z…f , 0 0;
, 1, … , ,  (3)
(h,h0 ) j (h,h )
(h,h0 )
(h,h0 ) h0
      ∑ ∑nj=1 z f 0 j λ j =,
z f 0 01,, …f (,h,h0;) = 1,1,. .…. ,, F(, h,h0 ) , ∀(h, h0 )
(h,h ) (h,h )
, θh ≤ 1, ,
      
∑ h = 1, . . . , k  (4) 
,
λhj ≥ 0, srh+ ≥, 0, r = 1, . . . , qh ; h = 1, . . . , k; j = 1, . . . , n
, ,
      
∑ , , 1, … , , , ∀ , , 
, ,
On the contrary, if the degree of dispersion between the outputs is very high, eyh becomes 1, and
the NEBM model changes into 0, the NSBM
1, … , ; model
1, …below
, ,  [30].

0, 1, … , ; 1, … ,     h+ 
q h sr
1/ρ∗ = maxλ,s+ ∑kh=1 Wh 1 + q1 ∑r= 1 yh ,
h ro
s.t. ∑nj=1 xijh λhj ≤ xio
h , i = 1, . . . , m ; h = 1, . . . , k
h
n
∑ j=1 yrj λ j − srh+ = yro
h h h , r = 1, . . . , q ; h = 1, . . . , k
h
(h,h0 ) (h,h0 ) (4)
∑nj=1 z f λhj = z f
(h,h0 ) j (h,h0 ) 0
(h,h0 ) 0 (h,h0 )
∑nj=1 z f λhj = z f , f (h,h0 ) = 1, . . . , F(h,h0 ) , ∀(h, h0 ).
(h,h0 ) j (h,h0 ) 0
λhj ≥ 0, srh+ ≥ 0, r = 1, . . . , qh ; h = 1, . . . , k; j = 1, . . . , n

The efficiency score of the NEBM model is between the efficiency scores of the NSBM and NCCR
models (ρ∗NSBM ≤ γ∗NEBM ≤ θ NCCR
∗ ). In this study, the NEBM model assumes constant returns to scale,
which leads to a lower number of efficient divisions than the variable returns to scale assumption.
Sustainability 2018, 10, 2852 7 of 16

For a particular DMU to be NEBM-efficient, all divisions must be NEBM-efficient; this increases the
discriminatory power of the NEBM model [15].

4. Data and Variables

Next, we applied the CCR model to 139 partners and the NEBM model to 540 supply chains.
Section 4.1 addresses the source of data. Section 4.2 selects the variables of each model to set the
network structure. Section 4.3 presents the results.

4.1. Data
Based on the available data from 2015, 139 partners, six subsidiaries, and two parent companies
that participate in a supply chain of the Hyundai Motor Company are selected as the sample; the total
number of supply chains which they create is 540. The partners are classified into the Hyundai
Motor Group’s affiliated and non-affiliated partners. They produce engines, transmission, car seats,
automatic control systems, and semiconductors for vehicles. Then, the subsidiaries, which are the
Hyundai Motor Group’s affiliated companies, semi-assemble the components from the partners to
manufacture modules and deliver them to the parent companies. The parent companies, Hyundai
Motor Company and Kia Motors, install a completed module onto the body frame of automobiles to
produce finished vehicles. The two parent companies share major components and produce different
lines of automobiles.
The data used in this study are collected from the Data Analysis, Retrieval and Transfer System
of the Financial Supervisory Service of Korea (dart.fss.or.kr), Korea Investor’s Network for Disclosure
(kind.krx.co.kr), KISLINE (www.kisline.com), job posting websites such as Career Catch (www.catch.co.kr)
and Job Korea (www.jobkorea.co.kr), the Hyundai Motor Company (www.hyundai.com), Korea Auto
Industries Coop. Association (www.kaica.or.kr), research reports of various securities firms, and finally,
official corporate websites and newsletters.

4.2. Variables
DEA sets an efficient frontier and evaluates the relative efficiency of DMUs based only on observed
data without any initial assumption for the production function. This makes the selection of adequate
inputs and outputs a highly important process. The validity and discriminatory power of a DEA
model exhibit a trade-off. The higher the number of inputs and outputs, the greater the amount of
data involved in performance evaluation. However, as more DMUs are positioned near the efficient
frontier, the discriminatory power in evaluating DMUs decreases [31]. The methods of minimizing the
loss of data and addressing the issue of the discriminatory power have been discussed. One of these
methods analyzes the correlation between variables to exclude the variables with a strong positive
correlation [32,33].
The DMUs of the CCR model are 139 partners. To choose the inputs and outputs, we examine
the extant literature on corporate performance evaluation that has applied DEA to the automobile
industry. Table 1 presents the inputs and outputs used in these studies. With reference to these data,
a correlation analysis is carried out to identify the relationship between the number of employees,
operating cost, cost of goods sold (COGS), total assets, fixed assets, and net worth and the relationship
between total gross sales, pre-tax profit, and operating profit. The variables with a strong correlation
are excluded from the inputs and outputs. The inputs for the CCR model comprise the number of
employees, operating cost, and fixed assets, while the output is the total gross sales. The number of
employees includes regular workers, non-regular workers, and administrative staff. The operating
cost is the selling, general and administrative (SG&A) expenses. The fixed assets are property, plant,
and equipment. The descriptive statistics of the inputs and outputs are provided in Table 2.
Sustainability 2018, 10, 2852 8 of 16

Table 1. Inputs and outputs of previous studies on the automobile industry. DMUs: decision-
making units.

Authors (year) DEA Model Inputs DEA Model Outputs Number of DMUs
1. Raw material
1. Labor
Saranga (2009) [14] 1. Gross income 34
3. Capital
4. Sundry expenses
1. Number of employees
Maritz (2013) [34] 1. Operating cost 1. Operating income 6
3. Gross asset
1. Net worth
Bhaskaran (2014) [35] 1. Employment 1. Annual sales 100
3. Fixed assets
1. COGS 1. Revenues
1. Operating expenses 1. Total equity
Wang et al. (2016) [36] 20
3. Fixed assets 3. Net incomes
4. Long-term investment
1. Raw materials cost
Sahoo and Rath (2018) 1. Labor cost
1. Total gross sales 20
[37] 3. Net fixed Asset
4. Energy cost

Table 2. Descriptive statistics of inputs and outputs.

Input Measures Output Measures

Number of Employees Operating Cost Fixed Assets Total Gross Sales
Average 474 35,044,057 146,943,202 446,351,875
Median 350 15,043,376 72,218,825 211,426,813
St. dev. 494 66,448,508 236,959,190 769,312,993
Max 4,307 501,529,108 1,784,196,568 5,558,080,871
Min 47 1,836,184 5,688,211 25,172,709

The DMUs of the NEBM model are 540 supply chains that comprise 139 partners, six subsidiaries,
and two parent companies, as shown in Figure 3. The inputs, outputs, and intermediate measures of
the supply chain should be selected from a comprehensive perspective of the supply chain, not from
a perspective of the individual company. Most partners are non-affiliated to Hyundai Motor Group;
that is, they operate independently, unlike subsidiaries and parent companies that cooperate with each
other. As the partners are not part of the Hyundai Motor Group, we measure the efficiency of the
division hs in terms of supplier selection.
Weber et al. [38] revealed that price, quality, and delivery performance are key criteria for
supplier selection. As reported in Table 3, the studies that have used DEA for supplier selection
generally take the price to be the input and the quality to be the output. For delivery performance,
delivery time is the input, while order fill rate is the output. In addition, other factors are taken
into consideration in evaluating suppliers, including finance, relationship, flexibility, technological
capability, and service [39]. However, since the partners are SMEs with a limited scope of
administration, the price is selected as the input, while the quality and delivery performance are
selected as the outputs. For the price, the operating profit ratio is used, which indicates the sales
margin of the partners. For the quality, the score of the quality rating system managed by Hyundai
Motor Company on its partners is used. For the delivery performance, the reciprocal value of the
finished inventory turnover ratio is used, which is one of the efficiency metrics for delivery in the North
American automotive supplier supply chain performance study [40]. A low inventory turnover ratio
of the partners which signifies excessive inventory enables stable supply of automobile components.
Sustainability 2018, 10, 2852 9 of 16

Sustainability 2018, 10, x FOR PEER REVIEW    9 of 16 

    ,     ,  

               
, ,
   
, ,
Division    Division    Division   
   

             

,         ,  

Figure 3. Supply chain structure. 
Figure 3. Supply chain structure.

Table 3. Inputs and outputs for supplier selection. 
Table 3. Inputs and outputs for supplier selection.
Authors (year) 
DEA Model Inputs  DEA Model Outputs 
Authors (year) 1. Price index 
DEA Model Inputs 1. Quality DEA Model Outputs
Liu et al. (2000) [41]  1. Delivery performance  1. Supplier variety 
1. Price index 1. Quality
3. Distance factor   
Liu et al. (2000) [41] 1. Delivery performance 1. Supplier variety
1. Quality 
3. Distance
Talluri et al. (2006) [42]  1. Price factor
1. Delivery performance 
1. Quality
1. Quality 
Talluri et al. (2006) [42] 1. Price
Ramanathan (2007) [43]  1. Total cost  1. Delivery performance
1. Service 
3. Technology 
1. Quality
Ramanathan (2007) [43] 1. Net price  1. Quality 
1. Service
1. Total cost
Hasan et al. (2008) [44]  1. Lead time  1. Quality benefits 
3. Technology
3. Service 
1. Net1. Price 
price 1. Quality
1. Quality 
Hasan et al. (2008) [44] 1. Lead time
Dotoli et al. (2016) [45]  1. Lead time  1. Quality benefits
1. Reliability 
3. Distance    3. Service
1. Price 1. Quality
For subsidiaries and parent companies, the number of employees and operating cost are selected 
Dotoli et al. (2016) [45] 1. Lead time 1. Reliability
3. Distance
as the inputs, while the total gross sales are selected as the output, with reference to Table 1. As parent 
companies are automobile export companies, the income from export sales is added as the output for 
parent companies. Material flow is selected as the intermediate product. The parameters of the supply 
For the subsidiaries and parent companies, the number of employees and operating cost are
chain are defined as below, and the descriptive statistics are provided in Table 4. 
selected as the inputs, while the total gross sales are selected as the output, with reference to Table 1.

As the parent
:  companies are automobile export
Operating profit ratio of the  companies, the income from export sales is added as the
th partner in the jth supply chain; 

output for

the parent companies. Material flow is selected as the intermediate measure.
Hyundai Motor Company’s five‐star quality evaluation score of the  The parameters
th partner in the 
: 
of the supply chain are defined as below, and the descriptive statistics are displayed in Table 4.
jth supply chain; 

:  Finished inventory turnover ratio of the  th partner in the jth supply chain; 
x1jhs : :  Operating profit ratio of the hs th partner in the jth supply
Numerator of the division in the partners level ( 1, … ,139); 
chain;

:  Hyundai Motor Company’s
The number of employees of the  five-star
th quality rating system score of the hs th partner in the
 subsidiary in the jth supply chain; 
y1jhs :
jth supply chain;
:  SG&A expenses of the  th subsidiary in the jth supply chain; 

y2jhs : :  Finished inventory turnover ratio of the hs th partner in the

Numerator of the division in the subsidiaries level ( jth…supply
140, ,145);  chain;
hs : Numerator of the division in the partners level (hs = 1, . . . , 139);
The number of employees of the 
:  th parent company in the jth supply chain; 

x1jhm : Number of employees of the hm th subsidiary in the jth supply chain;

:  SG&A expenses of the  th parent company in the jth supply chain; 
x2jhm : SG&A expenses of the hm th subsidiary in the jth supply chain;
:  Total gross sales of the  th parent company in the jth supply chain; 
hm :
Numerator of the division in the subsidiaries level (hm = 140, . . . , 145);
h :  Export sales of the  th parent company in the jth supply chain; 
x1j p : Number of employees of the h p th parent company in the jth supply chain;
hp :  Numerator of the division at the parent company level ( 146, 147); 
x2j : ,
SG&A expenses of the h p th parent company in the jth supply chain;
h :  Material flow from division h to division    ∀ h, . 
y1j p : , Total gross sales of the h p th parent company in the jth supply chain;
h  
y2j p : Export sales of the h p th parent company in the jth supply chain;
hp: Numerator of the division in the parent companies level (h p = 146, 147);
(h,h0 )
zf : Material flow from division h to division h0 (∀(h, h0 )).
(h,h0 ) j
Sustainability 2018, 10, 2852 10 of 16

Table 4. Descriptive statistics of inputs, outputs, and intermediate measures.

Division hs Division hm
DMU Input Outputs Inputs
x1jhs y1jhs y2jhs x1jhm x2jhm
Average 0.03324 83 0.03741 17 2,014,454
Median 0.03125 81 0.03313 3 426,982
St. dev. 0.02913 3 0.02421 53 5,833,982
Max 0.09906 90 0.12886 774 79,056,091
Min −0.19030 80 0.00119 1 617
Division hp
Intermediate
DMU Inputs Outputs
h h h h ( hs , hm ) ( hm , hp )
x1j p x2j p y1j p y2j p zf zf
( hs , hm ) j ( hm , hp ) j

Average 23 3,137,135 13,739,946 8,292,159 10,560,031 45,280,252

Median 5 765,046 2,905,442 1,716,136 2,235,820 13,664,184
St. dev. 61 8,564,540 39,287,726 23,319,977 30,030,282 101,100,765
Max 605 106,978,039 518,284,069 292,719,924 389,460,516 1,104,962,541
Min 1 1,855 4,355 2,789 3,432 57,931

4.3. Efficiency Analysis

Table 5 summarizes the efficiency scores of the CCR model for 139 partners and of the NSBM,
NEBM, and NCCR models for 540 supply chains. Table 6 also presents descriptive statistics of the
divisional efficiency scores of the NSBM, NEBM, and NCCR models for division hs . The result of the
CCR model shows that only two of the 139 partners obtain a CCR efficiency score of 1. The best practice
DMUs are S083 and S139, which form a reference set for other inefficient DMUs. The CCR efficiency
score is generally low; 88 partners have a score below the average (0.3011); S018, S034, and S040 have
a score less than 0.1.
The NEBM model is based on the assumptions of (a) constant returns to scale, (b) fixed links,
and (c) identical weight for all divisions. In the NEBM model, only the DMUs where all divisions are
efficient can be overall efficient [15]. As a result, there is no supply chain with an overall efficiency
score of 1. N043, N0245, and N0281 have the highest efficiency score (0.9866); we can attribute their
high overall efficiency score to the high divisional efficiency score of hs within the supply chain.
The divisional efficiency scores of the NEBM model for partner S010 within supply chain N043, partner
S081 within supply chain N0281, and partner S088 within supply chain N0245 are all 1. However,
these partners are evaluated as inefficient DMUs in the CCR model. The CCR efficiency scores are
0.1827 for S010, 0.1507 for S081, and 0.1263 for S088, which are all below the median value (0.2387).
Unlike the NSBM and NEBM models with zero efficient supply chains, 92 of the 540 supply chains
have the NCCR efficiency score of 1. This is because a radial model has a lower discriminatory power
than a non-radial model [46]. As described earlier, the eyh of some divisions has a positive value because
of the dispersion of the outputs. The overall efficiency score of the NEBM model is between the overall
efficiency scores of the NSBM and NCCR models (ρ∗NSBM ≤ γ∗NEBM ≤ θ NCCR ∗ ); the divisional efficiency
score of the NEBM model is also between the divisional efficiency scores of the NSBM and NCCR
∗ ∗ ∗
models (ρhNSBM ≤ γhNEBM ≤ θ hNCCR ). Meanwhile, the divisional efficiency scores of 81 hs s and one h p
in the NEBM and NCCR models are the same. This suggests that the eyh of these divisions equals 0.
As such, the NEBM model is sensitive to the dispersion of data; it evaluates DMUs considering both
radial and non-radial properties [29].
Sustainability 2018, 10, 2852 11 of 16

Table 5. Descriptive statistics of the CCR, network SBM (NSBM), network EBM (NEBM), and network
CCR (NCCR) efficiency scores.

Efficiency Score CCR NSBM NEBM NCCR

Average 0.30110 0.07224 0.09241 0.96253
Median 0.23869 0.03322 0.04605 0.98801
St. dev. 0.18046 0.12517 0.14512 0.03683
Max 1.0000 0.95424 0.98657 1.0000
Min 0.05976 0.00037 0.00041 0.90500
The number of efficient DMU 2 0 0 91

Table 6. Descriptive statistics of the hs divisional efficiency scores of the NSBM, NEBM, and
NCCR models.

Efficiency Score NSBM NEBM NCCR

Average 0.04592 0.05819 0.05901
Median 0.01693 0.02358 0.02421
St. dev. 0.11714 0.12732 0.12812
Max 1.0000 1.0000 1.0000
Min 0.00019 0.00020 0.00021

5. Determinant Factors of Automobile Supply Chain Efficiency

The Hyundai Motor Company promotes quality management through win–win cooperation
with its partners. It provides both financial support (raising the unit prices of components from
suppliers and paying for their labor costs) as well as non-financial support (investment in research and
development (R&D) and quality control of manufacturing process) to raise its partners’ competitiveness
and ensure the supply of quality components. This ultimately enhances the quality competitiveness of
its finished automobiles.
As the partnership with the Hyundai Motor Company strengthens, the unit prices the partner
receives increase compared to the raw material cost. Thus, this allows the partner to benefit from
high rates of return and improved efficiency. However, this leads to an increase in the cost of finished
vehicles, which causes inefficiency in the overall supply chain [10]. When a supply contract that
stipulates the terms and conditions for the supply of auto parts concludes, the partner maximizes
efficiency by reducing costs of the expected revenue. However, from a total supply chain perspective,
it is efficient to maximize the expected revenue by producing the maximum amount of high quality
automobiles with prepaid costs. The components produced by partners are intermediate measures
that are used as inputs for the next production process; therefore, their quality affects the driving
performance and durability of the finished vehicles. If partners compromise the quality of their
products to reduce costs, efficiency may increase at an individual company level, but it would decrease
at an entire supply chain level. In this context, the following hypothesis is established.

Hypothesis. The partner efficiency has a negative impact on the supply chain efficiency.

5.1. Mann–Whitney U Test

As DEA is a non-parametric method in which the efficiency score does not comply with
normal distribution, Golany [47] suggested applying the Mann–Whitney U test to the DEA results.
The Mann–Whitney U test is thus conducted to verify the existence of statistically significant difference
between the two groups divided in accordance with the aforementioned hypothesis. The results in
Table 7 reveal that the NEBM efficiency score is lower in the supply chain with partners that have
a CCR efficiency score of 1 than in the supply chain without such partners, at a significance level
of 0.05.
Sustainability 2018, 10, 2852 12 of 16

Table 7. Mann–Whitney U test.

Variable Mann–Whitney U
Mean Z Ratio p-Value
3.457 0.001 **
Involved CCR efficient partners 0.01214
Not involved 0.09393
Note: ** statistically significant at the 0.05 level.

5.2. Tobit Regression Model

Section 5.1 confirmed the difference in efficiency between supply chain groups with and without
efficient partners. Therefore, we must clearly identify the cause of this difference. Many studies have
used a regression analysis to examine the variables that affect the DEA efficiency scores [48]. However,
as the DEA efficiency score (a dependent variable) is limited to a value in [0,1] and exhibits a truncated
distribution from both sides, the ordinary least squares model generates biased and inconsistent
estimations [49]. The Tobit regression model is thus suggested to prevent this problem. It is used
when the dependent variables are bounded from below, above, or both [50]. The following equation
illustrates this model: 
∗ ∗
 yi i f 0 ≤ yi ≤ 1

∗
yi = βxi + ε i , yi = 0 i f yi∗ < 0 (5)

 ∗
1 i f yi > 1
where yi is the DEA efficiency score; yi∗ is a latent variable; xi is a vector of explanatory variables and
β is a vector of estimated parameters; ε i refers to error term independent and identically distributed
as N(0,σ2 ).
We now estimate β and σ, which maximize the likelihood function L based on observations. Hence,

L= ∏ P ( y i |0 < y i < 1) ∏ P ( y i = 0) ∏ P ( y i = 1) (6)

0< y i <1 y i =0 y i =1

where
(yi − βxi )2
1 −
P ( y i |0 < y i < 1) = √ e 2σ2 ,
2πσ2
Z − βx
1 i − t2
P ( y i = 0) = √ e
2σ2 dt,
2πσ2 −∞
Z −(1− βx ) 2
1 i − t2
P ( y i = 1) = √ 2σ e dt
2πσ2 −∞

In the Tobit regression model, the CCR efficiency score is an independent variable and the NEBM
efficiency score is a dependent variable. The results are consistent with those of the Mann–Whitney U
test. As Table 8 indicates, the CCR efficiency score of the partner has a negative impact on the NEBM
efficiency score of the supply chain. In other words, the efficiency of the partner within the supply
chain reduces the efficiency of the supply chain.

Table 8. Tobit regression model result.

Variable Coefficient Standard Error Z p-Value

Const. 0.153 0.012 12.815 0.000
CCRI −0.201 0.034 −5.885 0.000 **
Log(scale) 1.718 0.310 5.536 0.000 **
Note: ** statistically significant at the 0.05 level.
Sustainability 2018, 10, 2852 13 of 16

6. Conclusions
The main purpose of this study was to identify the impact of the partner efficiency on the overall
supply chain efficiency. Under the assumption that a supply contract that specifies the unit price
and quantity is signed between automakers and partners, an input-oriented CCR model was used to
measure the efficiency of the individual partner, while an output-oriented NEBM model was used
to measure the efficiency of the overall supply chain. Then, the relationship between the partner
efficiency and the supply chain efficiency was analyzed using the Mann–Whitney U test and the Tobit
regression model.
In the first phase, two types of DEA models were used. The CCR model was applied to assess the
competitiveness of the individual partner. The inputs comprised the number of employees, operating
cost, and fixed assets, while the output was the total gross sales. According to the results, only two
of the 139 partners were identified as efficient DMUs. The CCR efficiency scores are low in general,
and 63% of all partners (88 of 139) have a CCR efficiency score below the average. The NEBM model
was applied to a three-tier supply chain comprising partners, subsidiaries, and parent companies.
The inputs and outputs of the partners (non-affiliates of the Hyundai Motor Group) were selected
based on the vendor selection criteria. The input of the partners was price, whereas the outputs were
quality and delivery performance. The inputs of the subsidiaries and parent companies were the
number of employees and operating cost, while the outputs were the total gross sales and export sales.
The intermediate measure was material flow. The result of the NEBM model reveals that none of the
540 supply chains was located at the efficient frontier. As only the supply chains with all divisions
being efficient have an NEBM efficiency score of 1, the NEBM model has higher discriminatory power
than the NCCR model. In addition, the NEBM model was suitable for measuring the efficiency of
a complex supply chain as its similarity to the NSBM or NCCR models increased according to the
dispersion of data. It evaluated efficiency using both radial and non-radial measures.
Under a circumstance in which the unit price, quantity, quality standards, and other details are
set, we suppose that partners would maintain quality only to the minimum requirement to reduce
the production cost. However, as the quality of components corresponds to the output of the supply
chain, a hypothesis was established—the partner efficiency at cost reduction would have a negative
impact on the overall supply chain efficiency. This hypothesis was verified through non-parametric
and parametric methods.
In the second phase, the Mann–Whitney U test and the Tobit regression model were used.
Two groups were created: a supply chain with partners achieving a CCR efficiency score of 1 and
a supply chain without such partners. Then, the Mann–Whitney U test was conducted to verify
the difference in the distribution of the NEBM efficiency scores between the two groups. The Tobit
regression analysis was also conducted to identify the causal relationship between the CCR efficiency
score and the NEBM efficiency score. The supply chain comprising the partners with a CCR efficiency
score of 1 was less efficient than the supply chain without such partners. That is, the more efficient the
partner, the less efficient the total supply chain would be.
This finding implied a conflict of interests within a supply chain consisting of independent
companies and therefore supported similar studies reporting the lower performance of a supply chain
under non-cooperative assumption [10,26]. Moreover, the quality score of efficient partners was not
higher than that of inefficient partners, which is consistent with previous studies demonstrating that
efficient suppliers focus on cost reduction, not on quality improvement [24].
In contrast to the results reported here, a previous study claimed that automakers’ financial and
technical support to partners would reduce supply chain inefficiency [14]. This discrepancy could
be explained by differences in the industry environment. In the Indian automobile industry, manual
labor was a poor substitute for automated equipment, while manufacturing processes in the Korean
automobile industry were mostly automated to eliminate such inefficiencies.
The Hyundai Motor Company has increasingly pursued win–win cooperation with its partners
because of political pressure and labor-management conflicts. Thus, its business strategy aims to
Sustainability 2018, 10, 2852 14 of 16

increase its partners’ competitiveness and ultimately enhance the quality competitiveness of its finished
vehicles. However, as our study reveals, the efficient operation of partners impairs the efficiency of the
total supply chain. This suggests that the effects of quality improvement on the partners are lower
than the support provided by the Hyundai Motor Company. Considering our findings, the automobile
industry must review its partner management system (performance measurement and incentive
systems) to establish a truly efficient supply chain. From a managerial point of view, this could
give managers a deeper insight on designing and implementing supply chain integration. This
approach also leads policymakers to a more realistic assessment for developing evaluation criteria in
the automobile industry.
Even though this study utilized sharper efficiency estimates of a three-tier supply chain by
applying the NEBM model, it has some limitations. In evaluating suppliers within the supply
chain, a wider criterion can be adopted, while we only examined the key indices because of limited
data. This would involve intangible factors such as information sharing, technological innovation,
and partnership, aside from price, quality, and delivery performance. In addition, potential risks
always exist in the supply chain, including the demand, production and logistics risks, and such risks
may lead to data uncertainties. Thus, in future studies, methods such as fuzzy model can be used to
deal with uncertainties and establish an efficient supply chain.

Author Contributions: I.K. conceived and designed the experiments and data collection. Finally, the paper was
written by I.K. and revised by C.K. All authors read and approved the final manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Soosay, C.A.; Hyland, P. A decade of supply chain collaboration and directions for future research.
Supply Chain Manag. Int. J. 2015, 20, 613–630. [CrossRef]
2. Tavana, M.; Kaviani, M.A.; Di Caprio, D.; Rahpeyma, B. A two-stage data envelopment analysis model for
measuring performance in three-level supply chains. Measurement 2016, 78, 322–333. [CrossRef]
3. Spekman, R.E.; Kamauff, J.W., Jr.; Myhr, N. An empirical investigation into supply chain management:
A perspective on partnerships. Supply Chain Manag. Int. J. 1998, 3, 53–67. [CrossRef]
4. Handfield, R.B.; Bechtel, C. The role of trust and relationship structure in improving supply chain
responsiveness. Ind. Mark. Manag. 2002, 31, 367–382. [CrossRef]
5. Liker, J.K.; Choi, T.Y. Building deep supplier relationships. Harv. Bus. Rev. 2004, 82, 104–113.
6. Attaran, M.; Attaran, S. Collaborative supply chain management: The most promising practice for building
efficient and sustainable supply chains. Bus. Process. Manag. J. 2007, 13, 390–404. [CrossRef]
7. Kanda, A.; Deshmukh, S.G. Supply chain coordination: Perspectives, empirical studies and research
directions. Int. J. Prod. Econ. 2008, 115, 316–335.
8. Cao, M.; Zhang, Q. Supply chain collaboration: Impact on collaborative advantage and firm performance.
J. Oper. Manag. 2011, 29, 163–180. [CrossRef]
9. Ramanathan, U.; Gunasekaran, A. Supply chain collaboration: Impact of success in long-term partnerships.
Int. J. Prod. Econ. 2014, 147, 252–259. [CrossRef]
10. Liang, L.; Yang, F.; Cook, W.D.; Zhu, J. DEA models for supply chain efficiency evaluation. Ann. Oper. Res.
2006, 145, 35–49. [CrossRef]
11. Chen, C.; Yan, H. Network DEA model for supply chain performance evaluation. Eur. J. Oper. Res. 2011, 213,
147–155. [CrossRef]
12. Whitley, R. East-Asian and Anglo-American business systems. In Economic Dynamism in the Asia-Pacific;
Routledge: Abingdon, UK, 1998; Chapter 9; pp. 213–249.
13. Lee, S.-Y. Win-win collaboration and supplier manufacturing performance: The mediating effects of relational
social capital accumulation. Korean Manag. Rev. 2013, 42, 1105–1130.
14. Saranga, H. The Indian auto component industry–Estimation of operational efficiency and its determinants
using DEA. Eur. J. Oper. Res. 2009, 196, 707–718. [CrossRef]
Sustainability 2018, 10, 2852 15 of 16

15. Tavana, M.; Mirzagoltabar, H.; Mirhedayatian, S.M.; Saen, R.F.; Azadi, M. A new network epsilon-based
DEA model for supply chain performance evaluation. Comput. Ind. Eng. 2013, 66, 501–513. [CrossRef]
16. Rajesh, G.; Mallinga, P. Supplier selection based on AHP QFD methodology. Procedia Eng. 2013, 64, 1283–1292.
[CrossRef]
17. Wong, W.P.; Jaruphongsa, W.; Lee, L.H. Supply chain performance measurement system: A Monte Carlo
DEA-based approach. Int. J. Ind. Syst. Eng. 2008, 3, 162–188. [CrossRef]
18. Färe, R.; Grosskopf, S. Network DEA. Socio-Econ. Plan. Sci. 2000, 34, 35–49. [CrossRef]
19. Kao, C. Network data envelopment analysis: A review. Eur. J. Oper. Res. 2014, 239, 1–16. [CrossRef]
20. Lozano, S.; Gutiérrez, E.; Moreno, P. Network DEA approach to airports performance assessment considering
undesirable outputs. Appl. Math. Model. 2013, 37, 1665–1676. [CrossRef]
21. Zeydan, M.; Çolpan, C.; Çobanoğlu, C. A combined methodology for supplier selection and performance
evaluation. Expert Syst. Appl. 2011, 38, 2741–2751. [CrossRef]
22. Ha, S.H.; Krishnan, R. A hybrid approach to supplier selection for the maintenance of a competitive supply
chain. Expert Syst. Appl. 2008, 34, 1303–1311. [CrossRef]
23. Çelebi, D.; Bayraktar, D. An integrated neural network and data envelopment analysis for supplier evaluation
under incomplete information. Expert Syst. Appl. 2008, 35, 1698–1710. [CrossRef]
24. Talluri, S.; Vickery, S.K.; Droge, C.L. Transmuting performance on manufacturing dimensions into business
performance: An exploratory analysis of efficiency using DEA. Int. J. Prod. Res. 2003, 41, 2107–2123.
[CrossRef]
25. Manello, A.; Calabrese, G.G.; Frigero, P. Technical efficiency and productivity growth along the automotive
value chain: Evidence from Italy. Ind. Corp. Chang. 2015, 25, 245–259. [CrossRef]
26. Sadjadi, S.; Bayati, M.F. Two-tier supplier base efficiency evaluation via network dea: A game theory
approach. Int. J. Eng.-Trans. A Basics 2016, 29, 931–938.
27. Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res.
1978, 2, 429–444. [CrossRef]
28. Xu, X.; Cui, Q. Evaluating airline energy efficiency: An integrated approach with network epsilon-based
measure and network slacks-based measure. Energy 2017, 122, 274–286. [CrossRef]
29. Tone, K.; Tsutsui, M. An epsilon-based measure of efficiency in DEA–A third pole of technical efficiency.
Eur. J. Oper. Res. 2010, 207, 1554–1563. [CrossRef]
30. Tone, K.; Tsutsui, M. Network DEA: A slacks-based measure approach. Eur. J. Oper. Res. 2009, 197, 243–252.
[CrossRef]
31. Hollingsworth, B.; Parkin, D. The efficiency of Scottish acute hospitals: An application of data envelopment
analysis. Math. Med. Boil. A J. IMA 1995, 12, 161–173. [CrossRef]
32. Kao, C.; Chang, P.L.; Hwang, S.N. Data envelopment analysis in measuring the efficiency of forest
management. J. Environ. Manag. 1993, 38, 73–83. [CrossRef]
33. Saen, R.F.; Memariani, A.; Lotfi, F.H. The effect of correlation coefficient among multiple input vectors on the
efficiency mean in data envelopment analysis. Appl. Math. Comput. 2005, 162, 503–521. [CrossRef]
34. Maritz, A.; Shieh, C.J. Performance analysis of automobile industry in Taiwan with data envelopment
analysis. Int. J. Appl. Math. Stat. 2013, 38, 84–95.
35. Bhaskaran, E. The productivity analysis of Chennai automotive industry cluster. J. Inst. Eng. (India) Ser. C
2014, 95, 239–249. [CrossRef]
36. Wang, C.N.; Nguyen, X.T.; Wang, Y.H. Automobile industry strategic alliance partner selection:
The application of a hybrid DEA and grey theory model. Sustainability 2016, 8, 173. [CrossRef]
37. Sahoo, P.K.; Rath, B.N. Productivity growth, efficiency change and source of inefficiency: Evidence from the
Indian automobile industry. Int. J. Automot. Technol. Manag. 2018, 18, 59–74. [CrossRef]
38. Weber, C.A.; Current, J.R.; Benton, W.C. Vendor selection criteria and methods. Eur. J. Oper. Res. 1991,
50, 2–18. [CrossRef]
39. Choi, T.Y.; Hartley, J.L. An exploration of supplier selection practices across the supply chain. J. Oper. Manag.
1996, 14, 333–343. [CrossRef]
40. North American Automotive Supplier Supply Chain Performance Study; PricewaterhouseCoopers: London, UK,
2013. Available online: https://www.pwc.com/gx/en/automotive/assets/automotive-supplier-supply-
chain.pdf/ (accessed on 29 October 2016).
Sustainability 2018, 10, 2852 16 of 16

41. Liu, J.; Ding, F.Y.; Lall, V. Using data envelopment analysis to compare suppliers for supplier selection and
performance improvement. Supply Chain Manag. Int. J. 2000, 5, 143–150. [CrossRef]
42. Talluri, S.; Narasimhan, R.; Nair, A. Vendor performance with supply risk: A chance-constrained DEA
approach. Int. J. Prod. Econ. 2006, 100, 212–222. [CrossRef]
43. Ramanathan, R. Supplier selection problem: Integrating DEA with the approaches of total cost of ownership
and AHP. Supply Chain Manag. Int. J. 2007, 12, 258–261. [CrossRef]
44. Hasan, M.A.; Shankar, R.; Sarkis, J. Supplier selection in an agile manufacturing environment using data
envelopment analysis and analytical network process. Int. J. Logist. Syst. Manag. 2008, 4, 523–550. [CrossRef]
45. Dotoli, M.; Epicoco, N.; Falagario, M.; Sciancalepore, F. A stochastic cross-efficiency data envelopment
analysis approach for supplier selection under uncertainty. Int. Trans. Oper. Res. 2016, 23, 725–748.
[CrossRef]
46. Zhou, P.; Poh, K.L.; Ang, B.W. A non-radial DEA approach to measuring environmental performance. Eur. J.
Oper. Res. 2007, 178, 1–9. [CrossRef]
47. Brockett, P.L.; Golany, B. Using rank statistics for determining programmatic efficiency differences in data
envelopment analysis. Manag. Sci. 1996, 42, 466–472. [CrossRef]
48. Banker, R.D.; Zheng, Z.E.; Natarajan, R. DEA-based hypothesis tests for comparing two groups of decision
making units. Eur. J. Oper. Res. 2010, 206, 231–238. [CrossRef]
49. Tariq, Y.B.; Abbas, Z. Compliance and multidimensional firm performance: Evaluating the efficacy of
rule-based code of corporate governance. Econ. Model. 2013, 35, 565–575. [CrossRef]
50. Hoff, A. Second stage DEA: Comparison of approaches for modelling the DEA score. Eur. J. Oper. Res. 2007,
181, 425–435. [CrossRef]

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