A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment.
Alimardani, Maryam ; Zolfani, Sarfaraz Hashemkhani ; Aghdaie, Mohammad Hasan 等
Introduction
Supply chain management (SCM) is considered as one of the most
important aspects of production planning and control (Yigin et al. 2007)
and it has recently been taken into account by managers and researchers.
The main aim of SCM is to manage multiple relationships across the
supply chain (SC) regarding the entire flow of information, materials,
and services to fulfil customer demands in an efficient manner (Li, Wang
2007). Supply chains comprise potential suppliers, producers,
distributors, retailers and customers and etc. In this context,
suppliers have an important role in achieving the goal of supply chain
management. In this regard, the integration of strategic partnership
with suppliers with better performance is recommended within the SC,
while it leads to enhanced performance of the chain in many directions
such as costs reduction through waste elimination, continuous
improvement of quality to achieve zero defects, flexibility improvement
to meet the end-customer requirements, decrease lead time at different
stages of the SC (Amin, Razmi 2009).
Besides, the concept of agile supply chains (ASC) or networks has
recently attracted many businesses to efficiently and effectively
respond to increasingly dynamic and volatile markets. Whenever a dynamic
network of companies is formed, an agile supply chain is likely to need
to change frequently in response to rapidly changing business
environments (Wu et al. 2009). In ASC, the alignment of companies with
their supply partners is suggested, which leads to improved efficiency
of their operations, as well as working together to achieve the
necessary levels of agility throughout the entire supply chain (Wu,
Barnes 2011). Therefore, among different ASC issues, supply partner
selection process becomes more crucial to survive in today's highly
competitive and global environment.
Moreover, there is a wide set of reasons to regard supplier
selection process as the most appealing issue, to which numerous
researches have been dedicated. The repetitive nature of supplier
selection process and frequently changing customer demands lead to the
increase in the uncertainty and ambiguity of this decision-making
process, particularly in ASC. Therefore, in order to achieve the
successful operation of an ASC, an effective supply partner selection
becomes an essential process that may enhance effectiveness, efficiency,
quality, safety and profit. It should be noted that the importance and
complexity of partner selection has increased (Sarkar, Mohapatra 2006).
ASC partner selection has been defined as a process for identification
of an efficient combination of suppliers, producers and distributors,
depending on which the right mix and quantity of products and services
are provided to customers (Talluri, Baker 2002). In an ASC,
determination of key components of the supply network-e.g. suppliers,
producers, distribution centres, etc.--can be an extremely complex task
just as well as specification of their combination. In addition,
demanding and dynamic market conditions, in which organizational
decision-makers may have to consider a wide set of selection criteria
such as performance, cost, flexibility (Cagliano et al. 2004) may change
over time. Other important reasons of the supplier selection issue could
be listed as follows: the product quality which depends on the
organization's suppliers, the existence of several suppliers that
offer a wide range of choices for selecting supplier alternatives.
Hence, the partner selection process should be done quickly as well as
thoroughly (Arteta, Giachetti 2004).
Supplier selection problem has been expressed as a complex
decision-making process in nature due to variant parameters and diverse
aspects (Xia, Wu 2007; Razmi et al. 2009). In this regard, the authors
propose supplier selection process in agile environments as a multiple
attribute decision-making (MADM) problem. MADM approaches one of the
major categories of multiple criteria decision-making (MCDM) methods and
deals with the evaluation and selection of an alternative among other
alternatives (Zavadskas et al. 2009, 2010). As Technique for Order
Preference by Similarity to Ideal Solution (TOPSIS) (Hwang, Yoon 1981),
Elimination and Choice Translating Reality (ELECTRE) (Roy 1968), MUSA
(Grigoroudis, Siskos 2002), Vlse Kriterijumska Optimizacija I
Kompromisno Resenje (VIKOR) (Opricovic 1998), Complex Proportional
Assessment (COPRAS) (Zavadskas, Kaklauskas 1996), Complex Proportional
Assessment with Grey relations (COPRAS-G) (Zavadskas et al. 2008, 2009),
Additive Ratio Assessment (ARAS) (Zavadskas, Turskis 2010; Zavadskas et
al. 2012), Step-wise Weight Assessment Ratio Analysis (SWARA)
(Kersuliene et al. 2010), Factor Relationship (FARE) (Ginevicius 2011)
are the prominent MADM techniques in the related literature.
Inclusive complex criteria used in multi-stage decision-making
process are apposite for solving many problems (Zavadskas et al. 2012;
Tamosaitiene et al. 2013; Tamosaitiene, Gaudutis 2013).
In this paper, the authors attempt to provide a novel hybrid MADM
methodology for supplier selection in agile environments. The proposed
model comprises SWARA and VIKOR techniques for agile supplier selection
in order to respond to increasingly volatile markets and survive in the
highly competitive manufacturing milieus. Firstly, the SWARA method is
implemented to obtain the weights of agility criteria. And then, the
VIKOR method is used for evaluation and selection of the best/agile
supplier alternative according to the agility level of an organization.
The rest of the paper is structured as follows: Section 1 presents
the proposed integrated approach model, and SWARA and VIKOR methods are
elaborated as well. In Section 2, a real case-study is analysed to
validate the proposed model. Also, the proposed decision-making SWARA
and VIKOR results are presented in Section 2. Finally, some remarks and
future research directions are provided in the final section.
1. Proposed integrated SWARA-VIKOR methodology
In today's dynamic manufacturing milieus, enterprises deal
with dramatic and often unexpected changes, such as the increase of
product variety and complexity, shorter time frames to respond, and the
continual need to gain new capabilities through innovativeness (Sari et
al. 2008). In this era, companies must use every opportunity for
performance improvement. To do so, a close relationship between a firm
and its supply chain partners has been recommended to optimize its
business processes (Wu, Barnes 2011). Furthermore, the required products
are changed frequently as well as some partners. Hence, supplier
selection process as a key step in the formation of any supply chains
and especially in the agile supply chains, which are frequently
reconfigured, is to be studied applying effective techniques (Sari et
al. 2008).
Supply partner selection process is a multi-attribute
decision-making problem that comprises both qualitative and quantitative
factors. Consequently, a variety of reasons exist for using MADM
approaches for selecting an alternative. Firstly, MADM methods deal with
the selection process of the best alternative among candidates that is
done upon decision-maker preferences with respect to many
conflicting/contradictory qualitative and quantitative multiple
criteria. Secondly, determination and evaluation of all these factors is
a difficult task.
The aim of this paper is: using MADM approaches to assess and
choose the best supplier for a manufacturing company that produces a
variety of products. Therefore, the authors attempt to propose a
bi-level hybrid structure of the new multiple-attribute decision-making
(MADM) methods to discuss supplier selection process for the first time
in agile supply chains. The supposed integrated approach involves two
MADM procedures; Step-wise Weight Assessment Ratio Analysis (SWARA) and
Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR). In the
first level, SWARA technique is devoted to calculation of the weight of
a criterion and then, VIKOR is proposed to rank agile supplier
alternatives from the best to the worst. Fig. 1 describes the evaluation
procedure of this study, which consists of three main phases:
Phase I. After establishing the decision-making team, the most
important criteria for supplier selection is identified. Next, the
qualitative and quantitative criteria are defined. Finally, the project
team constructs the selection criteria and problem structure.
Based upon a comprehensive review of partner evaluation process,
and measurement of organizations agility, the authors propose the four
main criteria including performance (as a combination of quality, time,
and progress), cost (as a combination of caution cost, capital
expenditure, and operational expenditure), flexibility (including
product flexibility, product volume flexibility, multi-skilled and
flexible people, establishment flexibility, manufacture flexibility),
and finally-technology that is measured in terms of technical
features/characteristics, system reliability/availability, system
redundancy, compliance with international standards, interoperability
with other systems, future technology development (Sharifi, Zhang 1999;
Tam, Tummala 2001; Tsourveloudis, Valavanis 2002; Lin et al. 2006; Luo
et al. 2009; Buyukozkan, Cifci 2011) that contribute to the goal. Fig. 2
represents the selection criteria and problem structure. As depicted in
Fig. 2, on the next level are four criteria that are decomposed into
numerous sub-criteria and some of the proposed sub-criteria are also
divided into some other sub-criteria.
The quality dimension is decomposed into three sub-criteria
including the product quality (Sharifi, Zhang 1999; Luo et al. 2009),
which is measured by means of the ratio of the defected product to the
all product, service level (Wu et al. 2009; Luo et al. 2009), and
information quality (Buyukozkan, Cifci 2011), which is measured in terms
of information accessible to beneficiary (Luo et al. 2009), perfect
degree of enterprise information system (Lin et al. 2006). The time
dimension comprises delivery time (Sari et al. 2008; Buyukozkan, Cifci
2011), on-time response to request (Buyukozkan, Cifci 2011),
distribution time, and transportation time (Wu et al. 2009). The
progress criterion is assessed upon customer satisfaction as well as
customer-driven innovations (Sharifi, Zhang 1999).
[FIGURE 1 OMITTED]
The cost dimension is a combination of caution cost, which is
evaluated by means of risk or commitment (Sari et al. 2008), capital
expenditure and operational expenditure (Tam, Tummala 2001). It must be
noted that raw material cost is suggested as a sub-criterion of the
capital expenditure criterion, since the whole ASC seeks to minimize the
cost of raw material, which is supplied by various suppliers. Moreover,
since the whole ASC seeks to minimize the production costs (Wu et al.
2009), which are provided by manufacturing plants, they are regarded as
operational expenditure. Besides, operational expenditure is evaluated
depending on maintenance cost and support system cost (Tam, Tummala
2001).
The flexibility criterion is categorized into product flexibility
(Sharifi, Zhang 1999), product volume flexibility (Tsourveloudis,
Valavanis 2002), multi-skilled and flexible people (Sharifi, Zhang 1999)
including sub-criteria continuous training and development
(Tsourveloudis, Valavanis 2002) and establishment flexibility, which
expresses the complexity and flexibility of building new relationships
as well as breaking up old relationships (Wu et al. 2009), manufacture
flexibility that is appraised according to concurrent execution of
activities (Tsourveloudis, Valavanis 2002; Lin et al. 2006).The proposed
criteria related to the agile supplier selection problem are presented
in Table 1.
[FIGURE 2 OMITTED]
Phase II. Criteria weights were calculated by applying SWARA method
and based on expert evaluations.
Phase III. In this stage, all alternatives were evaluated by the
project team and VIKOR method was applied to achieve the final ranking
results.
The following weight assessment approaches are among those listed
in the literature: Entropy (Shannon 1948; Susinskas et al. 2011;
Kersuliene, Turskis 2011), FARE (Ginevicius 2011), SWARA (Kersuliene et
al. 2010), etc. SWARA method is one of the brand-new ones. In this
method, an expert plays an important role on evaluations and calculation
of weights. Also, each expert chooses the importance of each criterion.
Next, each expert ranks all criteria from the first to the last. An
expert uses his or her own implicit knowledge, information and
experiences. Based on this method, the most significant criterion is
given rank 1, and the least significant criterion is given rank last.
The overall ranks to the group of experts are determined according to
the mediocre value of ranks (Kersuliene, Turskis 2011). The ability to
estimate experts' opinion about importance ratio of the criteria in
the process of their weights determination is the main element of this
method (Kersuliene et al. 2010). Moreover, this method is helpful for
coordinating and gathering data from experts. Furthermore, SWARA method
is uncomplicated and experts can easily work together. The main
advantage of this method in decision-making is that in some problems
priorities are defined based on policies of companies or countries and
there is no need for evaluation to rank criteria. In other methods, such
as AHP or ANP, the model is created based on criteria and expert
evaluations will affect priorities and ranks (Zavadskas et al. 2011;
Hashemkhani Zolfani et al. 2012). So, SWARA can be useful for some
issues with known priorities depending on a situation; and finally,
SWARA is proposed in a certain environment of decision-making. All
developments of decision-making models based on SWARA method are as
follow: Kersuliene et al. (2010) in selection of rational dispute
resolution method; Kersuliene, Turskis (2011) for architect selection;
Hashemkhani Zolfani et al. (2013a) in design of products; Aghdaie et al.
(2013) in the machine tool selection; Hashemkhani Zolfani et al. (2013b)
in selecting the optimal alternative of mechanical longitudinal
ventilation of tunnel pollutants; Hashemkhani Zolfani et al. (2013c) in
investigating the success factors of online games based on explorer.
VIKOR method
The VIKOR method is a compromise MADM method, developed by
Opricovic, Tzeng (Opricovic 1998; Opricovic, Tzeng 2002). The concept of
VIKOR is based on the compromise programming of MCDM by comparing the
measure of "closeness" to the "ideal" alternative
(Wu et al. 2009). The VIKOR method can provide a maximum "group
utility" for the "majority" and a minimum of an
individual regret for the "opponent" (Opricovic 1998;
Opricovic, Tzeng 2002, 2004).
The recent developments of decision-making models based on VIKOR
method are listed below: Fouladgar et al. (2012) in project portfolio
selection, Yucenur and Demirel (2012) for insurance company selection,
Wang and Tzeng (2012) for creating brand value, Liu et al. (2012) in
improvement of tourism policy implementation, Wu et al. (2012) for
ranking universities, Antucheviciene et al. (2011) in ranking of
building redevelopment alternatives.
2. Case study
A real case study problem has been chosen to show the performance
and application of the model. The study was conducted by a well-known
company in manufacturing automobile industry. This company is located
near Tehran, in Iran and it is a large manufacturing company with more
than 500 employees. Besides, it is one of the biggest suppliers for both
Saypa and Zamyad automobile manufacturing companies. Recent fast changes
in automobile market environment and customer needs have been combined
with high competitiveness in this market place. Therefore, the company
has decided to use analytical tools for evaluation and selection of its
suppliers. After defining a new project for evaluation and selection of
suppliers, a project team of two industrial engineers, two managers and
CEO of the company was established (see Table 2). This team identified
four potential suppliers as alternatives for evaluation. The
alternatives denoted as [A.sub.1], [A.sub.2], [A.sub.3], and [A.sub.4],
respectively.
Among all criteria ten criteria [X.sub.l-1-1], [X.sub.l-1-2],
[X.sub.l-1-3], [X.sub.l-1-4], [X.sub.2-1], [X.sub.2-2-1], [X.sub.2-2-2],
[X.sub.2-2-3], [X.sub.4-4], and [X.sub.2-4], are cost criterion (the
minimum amount of this criterion is desirable) and others are benefit
criteria. This kind of classification is important for VIKOR analysis.
Decision-making team has followed every step of this project for
this selection. They accepted the criteria list for evaluation of
alternatives, which were derived from the literature survey. Also, they
developed the problem structure (see Fig. 2).
For receiving general agreement in every step of this project,
Delphi method was used. Delphi is a very famous method for receiving
general agreement in complicated decision-making situations. Therefore,
after a numerous discussions, a project team identified criteria for
evaluation and they constructed problem structure. Then the project team
accepted the criteria list that was explored from the literature study
(see Table 1). There was a general consensus about this criteria list.
As mentioned before, in this paper SWARA was used for calculating
criteria weights.
In this section, the authors focus on obtained numerical results.
In the first part, SWARA results will be discussed. As mentioned before,
after determining all selection criteria and supplier alternatives,
SWARA method was used to tackle the ambiguities involved in the process
of the linguistic assessment of the criteria and alternatives. Like
other similar methods (e.g. AHP and ANP), SWARA uses expert ideas or
thoughts but experts can participate without difficulty in this method.
Information about experts is shown in Table 2. Table 3 shows criteria
weights and the decision matrix that is filled by experts. Also, Table 3
indicates the results of criteria weights for all assessment criteria,
criteria and sub-criteria. The weight of each criterion is shown in the
fifth column. The last column of Table 3 provides the evaluations of
each alternative by experts that are used to calculate the rank of each
alternative. Table 3 is used as an input, which is applied by VIKOR
method. The aim of using VIKOR method is selecting the best supplier.
After discussing SWARA results, in this section, the authors ranked
suppliers based on VIKOR. Equations in VIKOR section were used for
calculations in VIKOR method.
The authors had four alternatives in this paper and there were four
potential suppliers as alternatives for evaluation. The alternatives
were denoted as [A.sub.1], [A.sub.2], [A.sub.3], and [A.sub.4]. Five
decision-making experts evaluated each alternative giving a score. After
creating the decision matrix, the normalized value was calculated and
other steps based on VIKOR steps were followed (Opricovic 1998;
Opricovic, Tzeng 2002, 2004).
According to Table 4, which shows ultimate results of VIKOR
methodology Alternative 3 (supplier 3) is the best option for this
problem. Based on this Table, this supplier can work and satisfy
company's needs in an agile environment. Also, the proposed hybrid
model provides a systemically analytic model for supplier selection in
an agile environment.
Conclusion and future research directions
In the current era, many businesses have been forced to form a
dynamic network of companies, namely agile supply chain, and outsourcing
has also increased to help businesses concentrate on frequent market
changes. In this regard, supply partner selection process becomes more
crucial in today's highly competitive and global environment. The
uncertainty and ambiguity of supplier selection process is the main
reason to suppose an effective supply partner selection to achieve the
successful operation of an ASC. To do so, in this paper, a hybrid MADM
methodology with three phases based on integrating two MADM methods for
selecting the most suitable supplier, was proposed. According to the
results of this study, DMs were faced with critical factors that were
found to influence an organization's decisions about evaluating and
selecting a new supplier. According to the results, the case study is
presented. Specifically, this study provides a valuable view that DMs
should be selected as a decision-making team. In addition, SWARA method
was used as a decision-making tool for extracting weights of criteria,
which VIKOR needed. Therefore, VIKOR used SWARA result weights as input
weights. Therefore, another significant contribution to this study is
the proposed SWARA-VIKOR integrated approach. In general, the findings
of this study have contributed towards providing important and advanced
knowledge by various criteria and a simple, efficient method, with which
managers of a company or decision-makers can increase their ability to
choose an appropriate supplier. As a result of the study, the authors
found that the proposed approach is practical for ranking supplier
alternatives with respect to multiple conflicting criteria in an agile
environment.
This study results show that decision criteria significantly
influence on the choice of supplier selection. However, in this paper
the most important criteria were selected based on the in-depth
literature survey; another study could design a new structure with other
criteria, sub-criteria and assessing alternatives with a new structure.
Caption: Fig. 1. The evaluation procedure
Caption: Fig. 2. Problem structure, selection aspects and
formulated alternatives
doi:10.3846/20294913.2013.814606
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Maryam ALIMARDANI (a), Sarfaraz HASHEMKHANI ZOLFANI (b), Mohammad
Hasan AGHDAIE (c), Jolanta TAMOSAITIENE (d)
(a) Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
(b) Department of Management, Science and Technology, Technology
Foresight Group, Amirkabir University of Technology (Tehran
Polytechnic), Futures Studies Research Institute, Amirkabir University
of Technology (Tehran Polytechnic), P.O. Box 1585-4413, Tehran, Iran
(c) Department of Industrial Engineering, Shomal University, P. O.
Box 731, Amol, Mazandaran, Iran
(d) Department of Construction Technologies and Management, Vilnius
Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius,
Lithuania
Corresponding author Jolanta Tamosaitiene
E-mail: jolanta.tamosaitiene@vgtu.lt
Received 27 December 2012; accepted 17 June 2013
Maryam ALIMARDANI received her Bachelor's degree in Industrial
Engineering-Industrial Production from Shomal University, Amol, Iran in
2009. She received her Master's degree in Industrial Engineering
Industrial Engineering from University of Tehran, Tehran, Iran in 2012.
Her papers have appeared in international conferences and journals, such
as International Journal of Production Research, International Journal
of Civil and Structural Engineering, Zadeh Journal of Mathematics. Her
major research interests include production planning and control, supply
chain management, supply chain inventory management, multi criteria
decision-making.
Sarfaraz HASHEMKHANI ZOLFANI received his Bachelor's degree in
Industrial Management and Master's degree in Industrial Engineering
from Shomal University, Iran. He is a PhD student of Technology
Foresight in Amirkabir University of Technology (Tehran Polytechnic). He
is working at Future Studies Research Institute of Amirkabir University
of Technology (Tehran Polytechnic), Sustainability office of Amirkabir
University of Technology (Tehran Polytechnic) and Research Institute of
the Internet and Intelligent Technologies, Vilnius Gediminas Technical
University. He is a member of EURO Working Group OR in Sustainable
Development and Civil Engineering. He is a reviewer in journals like:
International Journal of Strategic Property Management, International
Journal of Business and Society etc. He is an author of more than 45
scientific papers that presented, published or reviewed at/for
International Conferences and Journals (including ISI-cited
publications). He has published in journals such as: Technological and
Economic Development of Economy, Journal of Business Economics and
Management, International Journal of Strategic Property Management,
Archives of Civil and Mechanical Engineering, Transport, The Baltic
Journal of Road and Bridge Engineering etc. His research interests
include: performance evaluation, strategic management, decision-making
theory, supply chain management, (fuzzy) multi criteria decision making,
marketing, future studies, sustainable development.
Mohammad Hasan AGHDAIE received his Bachelor's and
Master's degreed in Industrial Engineering from Shomal University,
in Amol. He is the author of more than 21 scientific papers in
international conferences and international journals, which were
published, accepted or peer-reviewed. He has published in journals such
as Journal of Business Economics and Management, International Journal
of Business Innovation and Research, The Baltic Journal of Road and
Bridge Engineering, Quarterly journal of Research and Planning in Higher
Education, Engineering Economics, and several others. His current
research interests include operations research, decision analysis,
multiple criteria decision analysis, operations research interfaces with
other fields, especially marketing, market segmentation, marketing
research and modelling, market design and engineering, pricing, data
mining, data science, application of fuzzy sets and systems, creative
thinking and problem solving.
Jolanta TAMOSAITIENE. Associate Professor, Dr, a Vice-Dean of Civil
Engineering Faculty and working in the Department of Construction
Technology and Management at Vilnius Gediminas Technical University,
Lithuania. Since 2013 is a member of Editorial Board "The Journal
of Engineering, Project, and Production Management", since 2011 is
a member of Editorial Board "Technological and Economic Development
of Economy" journal. Since 2009 is a member of EURO Working Group
OR in Sustainable Development and Civil Engineering, EWG-ORSDCE. Since
2013 is a board member of Engineering, Project, and Production
Management Association. She published 50 scientific papers. Research
interests: many miscellaneous management areas (enterprise, construction
project etc.), risk assessment, construction project administration,
building life-cycle, construction technology and organisation,
decision-making and grey system theory, Decision Making (DM),
statistics, optimization, strategies, game theory, intelligent support
system, Sustainable Development: developing of alternative construction
processes, economic and other aspects, sustainable development
challenges for business and management in construction enterprises,
environmental impact processes etc.
Table 1. Factors taken from the review of the related literature
and relevant to supplier evaluation and selection in an agile
supply chain
No. Criteria and Related literature source
sub-criteria
[X.sub.1] Performance
[X.sub.1-1] Quality
[X.sub.1-1-1] product quality Wu et al. (2009); Luo
et al. (2009)
[X.sub.1-1-2] service level Wu et al. (2009); Luo
et al. (2009)
[X.sub.1-1-3] information quality Buyukozkan, Cifci (2011)
[X.sub.1-2] Time
[X.sub.1-2-1] delivery time Sari et al. (2008);
Buyukozkan, Cifci (2011)
[X.sub.1-2-2] on-time response to Luo et al. (2009);
request Buyukozkan, Cifci (2011)
[X.sub.1-2-3] distribution time Wu et al. (2009)
[X.sub.1-2-4] transportation time Wu et al. (2009)
[X.sub.1-3] Progress
[X.sub.1-3-1] customer satisfaction
[X.sub.1-3-2] customer-driven Sharifi, Zhang (1999)
innovations
[X.sub.2] Cost
[X.sub.2-1] Caution cost Sari et al. (2008)
[X.sub.2-2] Capital expenditure Tam, Tummala (2001); Wu
et al. (2009)
[X.sub.2-3] Operational
expenditure
[X.sub.2-3-1] production cost Wu et al. (2009)
[X.sub.2-3-2] maintenance cost Tam, Tummala (2001)
[X.sub.2-3-3] support system cost Tam, Tummala (2001)
[X.sub.3] Flexibility
[X.sub.3-1] Product flexibility Sharifi, Zhang (1999)
[X.sub.3-2] Product volume Tsourveloudis,
flexibility Valavanis (2002)
[X.sub.3-3] Multi-skilled and
flexible people
[X.sub.3-3-1] continuous training Tsourveloudis,
and development Valavanis (2002)
[X.sub.3-3-2] employee skills
utilization
[X.sub.3-4] Establishment Wu et al. (2009)
flexibility
[X.sub.3-5] Manufacture Tsourveloudis, Valavanis
flexibility (2002); Lin et al. (2006)
[X.sub.4] Technology
[X.sub.4-1] Technical features/ Sharifi, Zhang (1999);
characteristics Buyukozkan, Cifci (2011)
[X.sub.4-2] System reliability/ Tam, Tummala (2001); Lin
availability et al. (2006)
[X.sub.4-3] System redundancy Tam, Tummala (2001)
[X.sub.4-4] Compliance with Tam, Tummala (2001); Luo
international et al. (2009)
standards
[X.sub.4-5] Interoperability Tam, Tummala (2001);
with other systems Tsourveloudis, Valavanis
(2002)
[X.sub.4-6] Future technology Sharifi, Zhang (1999);
development Tam, Tummala (2001)
Table 2. The characteristics of the five decision-making experts
Decision-making expert
Gender Age Education Experience
Level (years)
D1 Male 53 B Sc in > 30
management
D2 Male 50 M Sc in business > 25
administration
D3 Female 49 M Sc in > 21
business
administration
D4 Male 45 B Sc in industrial > 18
engineering
D5 Female 47 Ph D > 14
of philosophy's
industrial
engineering
Gender Job title Job responsibility
D1 Male Manager of In charge of the most
the company important decisions of the
(CEO) company.
D2 Male Supply chain Managing the engineering
manager team, supply chain, suppliers
and new projects.
D3 Female Operations Managing, designing, and
manager controlling the process of
production and redesigning
business operations in the
production of goods and/or
services.
D4 Male Production Managing product lines,
planning buying new materials and
and material inventory planning.
handling
manager
D5 Female Marketing Responsible for R&D,
manager marketing research and
pricing decisions.
Table 3. Calculation results by applying SWARA and VIKOR
methods
Criteria weights based on SWARA
Criterion Comparative Coefficient
importance [k.sub.j] =
of average [s.sub.j] + 1
value
[s.sub.j]
[X.sub.1] 0.28 1.28
[X.sub.1-1] 1
[X.sub.1-3] 0.25 1.25
[X.sub.1-2] 0.21 1.21
[X.sub.1-1-1] 0.13 1.13
[X.sub.1-1-2] 0.16 1.16
[X.sub.1-1-3] 0.18 1.18
[X.sub.1-1-4] 1
[X.sub.1-2-1] 1
[X.sub.1-2-2] 0.20 1.20
[X.sub.1-3-1] 0.26 1.26
[X.sub.1-3-2] 1
[X.sub.1-3-3] 0.12 1.12
[X.sub.2] 0.32 1.32
[X.sub.2-1] 0.39 1.39
[X.sub.2-2] 1
[X.sub.2-2-1] 0.21 1.21
[X.sub.2-2-2] 0.1 1.1
[X.sub.2-2-3] 1
[X.sub.2-3] 0.27 1.27
[X.sub.3] 0.20 1.20
[X.sub.3-1] 1
[X.sub.3-2] 0.26 1.26
[X.sub.3-3] 0.42 1.42
[X.sub.3-3-1] 1
[X.sub.3-3-2] 0.31 1.31
[X.sub.3-4] 0.18 1.18
[X.sub.3-5] 0.23 1.23
[X.sub.4] 1
[X.sub.4-1] 0.19 1.19
[X.sub.4-2] 0.30 1.3
[X.sub.4-3] 0.35 1.35
[X.sub.4-4] 0.16 1.16
[X.sub.4-5] 1
[X.sub.4-6] 0.26 1.26
Criteria weights based on SWARA
Criterion Recalculated Weight
weight [q.sub.j] =
[w.sub.j] = [w.sub.j] /
[x.sub.j-1]/ [SIGMA]
[k.sub.j] [w.sub.j]
[X.sub.1] 0.781 0.272
[X.sub.1-1] 1 0.406
[X.sub.1-3] 0.8 0.326
[X.sub.1-2] 0.662 0.268
[X.sub.1-1-1] 0.884 0.270
[X.sub.1-1-2] 0.645 0.196
[X.sub.1-1-3] 0.749 0.228
[X.sub.1-1-4] 1 0.306
[X.sub.1-2-1] 1 0.545
[X.sub.1-2-2] 0.833 0.455
[X.sub.1-3-1] 0.793 0.317
[X.sub.1-3-2] 1 0.400
[X.sub.1-3-3] 0.708 0.283
[X.sub.2] 0.591 0.207
[X.sub.2-1] 0.566 0.241
[X.sub.2-2] 1 0.424
[X.sub.2-2-1] 0.751 0.284
[X.sub.2-2-2] 0.909 0.341
[X.sub.2-2-3] 1 0.375
[X.sub.2-3] 0.787 0.335
[X.sub.3] 0.492 0.171
[X.sub.3-1] 1 0.294
[X.sub.3-2] 0.384 0.113
[X.sub.3-3] 0.484 0.143
[X.sub.3-3-1] 1 0.568
[X.sub.3-3-2] 0.763 0.432
[X.sub.3-4] 0.847 0.248
[X.sub.3-5] 0.688 0.202
[X.sub.4] 1 0.350
[X.sub.4-1] 0.478 0.136
[X.sub.4-2] 0.569 0.169
[X.sub.4-3] 0.740 0.211
[X.sub.4-4] 0.326 0.093
[X.sub.4-5] 1 0.286
[X.sub.4-6] 0.379 0.108
Decision matrix on VIKOR
Criterion [A.sub.1] [A.sub.2]
[X.sub.1]
[X.sub.1-1]
[X.sub.1-3]
[X.sub.1-2]
[X.sub.1-1-1] 9 5
[X.sub.1-1-2] 4 8
[X.sub.1-1-3] 4 4
[X.sub.1-1-4] 8 3
[X.sub.1-2-1] 9 3
[X.sub.1-2-2] 6 9
[X.sub.1-3-1] 5 6
[X.sub.1-3-2] 9 6
[X.sub.1-3-3] 4 8
[X.sub.2]
[X.sub.2-1] 4 6
[X.sub.2-2]
[X.sub.2-2-1] 8 5
[X.sub.2-2-2] 2 4
[X.sub.2-2-3] 4 3
[X.sub.2-3] 6 4
[X.sub.3]
[X.sub.3-1] 3 8
[X.sub.3-2] 4 5
[X.sub.3-3]
[X.sub.3-3-1] 4 9
[X.sub.3-3-2] 6 5
[X.sub.3-4] 9 6
[X.sub.3-5] 6 4
[X.sub.4]
[X.sub.4-1] 5 4
[X.sub.4-2] 8 6
[X.sub.4-3] 3 4
[X.sub.4-4] 5 6
[X.sub.4-5] 6 7
[X.sub.4-6] 9 4
Decision matrix on VIKOR
Criterion [A.sub.3] [A.sub.4]
[X.sub.1]
[X.sub.1-1]
[X.sub.1-3]
[X.sub.1-2]
[X.sub.1-1-1] 4 8
[X.sub.1-1-2] 9 6
[X.sub.1-1-3] 6 3
[X.sub.1-1-4] 5 3
[X.sub.1-2-1] 4 6
[X.sub.1-2-2] 8 9
[X.sub.1-3-1] 7 8
[X.sub.1-3-2] 3 4
[X.sub.1-3-3] 8 5
[X.sub.2]
[X.sub.2-1] 5 8
[X.sub.2-2]
[X.sub.2-2-1] 3 4
[X.sub.2-2-2] 3 6
[X.sub.2-2-3] 3 6
[X.sub.2-3] 3 4
[X.sub.3]
[X.sub.3-1] 4 9
[X.sub.3-2] 3 8
[X.sub.3-3]
[X.sub.3-3-1] 6 5
[X.sub.3-3-2] 8 4
[X.sub.3-4] 8 4
[X.sub.3-5] 4 7
[X.sub.4]
[X.sub.4-1] 8 4
[X.sub.4-2] 9 7
[X.sub.4-3] 5 3
[X.sub.4-4] 7 4
[X.sub.4-5] 8 3
[X.sub.4-6] 4 9
Table 4. Ultimate results and ranking of the alternatives
Alternatives [s.sub.i] [k.sub.j] [w.sub.j]
[A.sub.1] 0.541 0.074 0.5
[A.sub.2] 0.496 0.058 0.5
[A.sub.3] 0.350 0.042 0.5
[A.sub.4] 0.569 0.100 0.5
Alternatives [q.sub.j] Ranking
[A.sub.1] 0.713 3
[A.sub.2] 0.472 2
[A.sub.3] 0 1
[A.sub.4] 1 4
