PME

I
J

http://polipapers.upv.es/index.php/IJPME

International Journal of 
Production Management 
and Engineering

https://doi.org/10.4995/ijpme.2022.17169 

Received: 2022-02-15 Accepted: 2022-06-27

Evaluation of supply chain risks by fuzzy DEMATEL method: 
a case study of iron and steel industry in Turkey

Asuman Üstündağ a1 , Sinan Çıkmak b* , Merve Çankaya Eyiol c , Mustafa Cahit Ungan a2

a School of Business, Sakarya University, Esentepe Campus 54187, Sakarya/Turkey.
b Social Sciences Vocational School, Duzce University, Uzunmustafa St, 81600, Duzce/Turkey.
c Graduate School of Business, Sakarya University, Esentepe Campus 54187, Sakarya/Turkey.

 a1 austundag@sakarya.edu.tr, b sinancikmak@duzce.edu.tr, c cankayamerve@gmail.com, a2 ungan@sakarya.edu.tr

Abstract:
Business practices to strengthen competitiveness increase the vulnerability of supply chains to risks. Risks that 
can adversely affect the effectiveness and efficiency of supply chain activities are events that disrupt the flow of 
information, materials, money, and products. Therefore, supply chain risk management is vital for companies. 
It is necessary to identify the risks that threaten the supply chain and prioritize them. In addition, examining the 
effects of risks on each other will determine the success of supply chain risk management. This study evaluates 
Turkey’s leading iron and steel company’s supply chain risk groups and sub-risks. The fuzzy DEMATEL method 
was used to determine the relative importance of the risks and the effects of the risks on each other. Results 
show that the most critical risk group is business risks. Business risk is followed by customer risks, supplier 
risks, transportation risks, environmental risks, and, finally, security risks. This study provides originality by 
evaluating the supply chain risks from a broader perspective.

Key words:
Fuzzy DEMATEL, iron and steel industry, risk assessment, supply chain risk management. 

1. Introduction

Almost every industry is exposed to increasing 
competitive pressure with the globalization of its 
business environment and markets. Companies 
tend to follow practices such as outsourcing, 
overseas manufacturing, lean manufacturing, 
inventory reduction, and supply chain collaboration. 
Although these practices strengthen companies’ 
competitiveness, they increase the vulnerability 
of their supply chains to risks. In other words, 
companies are increasingly exposed to unexpected 
disruptions that affect the entire supply chain (Munir 
et al., 2020).

A disruption at one stage of a supply chain will 
affect the entire chain and negatively affect firms’ 
different levels (Parast & Subramanian, 2021). The 
earthquake and the tsunami in Japan in 2011 caused 
an interruption in supply and demand and therefore 
slowed production in other countries (Tukamuhabwa 
et al., 2015). Recent crises such as natural disasters 
and epidemics have significantly interrupted supply 
chain activities. The COVID-19 pandemic has 
severely disrupted supply chains globally and locally 
(Pujawan & Bah, 2022). Due to the global epidemic, 
companies operating in different geographies but 
with the same supply chain have been interrupted 
in their production capabilities. Not only risks at 

To cite this article: Üstündağ, A., Çıkmak, S., Çankaya Eyiol, M., Ungan, M.C. (2022). Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron 
and steel industry in Turkey. International Journal of Production Management and Engineering, 10(2), 195-209. https://doi.org/10.4995/ijpme.2022.17169 

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the worldwide level, but companies may also face 
machine breakdowns, exchange rate fluctuation, 
low supplier integration, inaccurate shipment from 
suppliers, inaccurate shipment to customers, and 
order fluctuation in their daily operational processes 
(Dong & Cooper, 2016).

Supply chain risk management, which includes 
the identification, evaluation, and management of 
risks in supply chain processes that are critical to 
business performance, has become an important 
area in supply chain management research (Ceryno 
et al., 2015; Dong & Cooper, 2016; Ho et al., 
2015). While previous research concentrated on 
supply chain risk management (Hermoso-Orzáez, 
& Garzón-Moreno, 2021; Ho et al., 2015; Mital 
et al., 2018; Zimmer et al., 2017), classification of 
risks (Alora & Barua, 2022; Duong et al., 2022; 
Kumar et al., 2020; Oke & Gopalakrishnan, 2009; 
Rangel et al., 2015) and assessments of risks (Ali 
et al., 2021; Alora & Barua, 2022; Mital et al., 
2018; Zimmer et al., 2017), only a few studies 
(Khan et al., 2021a; Pfohl et al., 2011; Sharma & 
Routroy, 2016) have examined the relationships 
between risks. In practice, supply chain risks are 
generally related, but this situation is ignored in 
traditional risk management. It should be stressed 
that revealing the relationships between risks 
will help decision-makers determine appropriate 
mitigation strategies and achieve more successful 
risk management outcomes.

To manage various operations of an industry, there is 
a need to understand the links between risks (Lahane 
& Kant, 2021). Although the supply chain structures 
are generally similar, some sectoral differences 
exist. Therefore, it is possible to mention that not all 
supply chains have the same types of risks (Gurtu & 
Johny, 2021; Hermoso-Orzáez, & Garzón-Moreno, 
2021; Srivastava & Rogers, 2021). For this reason, 
it would be more beneficial to focus on a particular 
industry to determine the causal relationships 
between risks more accurately. In this context, the 
study focuses on the supply chain risks of Turkey’s 
iron and steel industry. The iron and steel industry 
is of great importance to the overall performance of 
the manufacturing industry in Turkey due to its high 
production and export potential and inputs to other 
sectors (Kabak et al., 2016). With this background, 
this study seeks answers to the following research 
questions:

RQ 1. What are the main risk groups in the supply 
chain and the sub-risks?

RQ 2. What is the relative importance of the risk 
groups and sub-risks?

RQ 3. What is the interrelationship between supply 
chain risks?

The rest of this paper is organized as follows. Section 
2 discusses the main concepts of the study. The fuzzy 
DEMATEL method is discussed in Section 3. Section 
4 introduces the results of the research. Finally, 
conclusions and discussions are given in Section 5.

2. Literature review

2.1. Supply chain risk management
Risk is a phenomenon that can affect the efficiency 
of an organization’s key processes (Hopkin, 2018). 
A supply chain risk is damage or loss resulting from 
supply chain disruption. Supply chain disruption is 
an undesirable, abnormal triggering event that occurs 
somewhere in or out of the supply chain (Wagner & 
Bode, 2008). Ho et al. (2015) define supply chain 
risk as: “the probability and impact of unexpected 
macro and/or micro-level events that adversely affect 
any part of the supply chain, leading to operational, 
tactical or strategic level failures or irregularities.”

Supply chain risk management has emerged to 
implement various strategies and practices to 
manage supply chain networks. Assessing the 
risks and reducing vulnerabilities (Gurtu & Johny, 
2021; Oturakçı & Yıldırım, 2022; Rajesh & Ravi, 
2017) will help to improve supply chain safety and 
performance. Finding ways to mitigate the effects 
of supply chain risks is critical for successfully 
managing supply chains in a volatile environment 
(Hachicha & Elmsalmi, 2014). In recent years, 
supply chain risk management has received more 
attention to overcome threats and challenges (Can 
Saglam et al., 2020). Generally, supply chain 
risk management is considered a phased process 
and consists of four steps: risk identification, risk 
assessment, risk mitigation, and risk monitoring (Ho 
et al., 2015). This study discusses the first two steps 
of supply chain risk management.

2.2. Supply chain risks identification
The first step of the supply chain risk management 
is identifying risk types. It is essential to understand 
the firm’s internal and external dynamics to evaluate 
the potential for supply chain disruptions (Sreedevi 
et al., 2021; Srivastava & Rogers, 2021). At this step, 

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managers focus on recognizing and clearly defining 
all risks. Thus, decision-makers become conscious 
of the events that cause uncertainty (Hallikas 
et al., 2004). Resources such as literature review, 
brainstorming, expert opinions, and examination of 
past events can be used to identify risks. In addition 
to qualitative methods, quantitative methods are also 
used to identify potential supply chain risks (Ho 
et al., 2015).

With the literature review and expert opinions, it is 
possible to talk about the existence of more than a 
hundred risks affecting businesses (Venkatesh et al., 

2015). To provide an overview, the supply chain risk 
classifications that different researchers included in 
their studies are shown in Table 1.

In some studies, risks are classified into two basic 
categories: internal risks and external risks. While 
internal risks are related to the activities in the supply 
chain processes, external risks consist of macro risks 
outside the supply chain and not under the control 
of the enterprise. Some other studies classify supply 
chain risks into three categories: internal risks, 
external risks within the supply chain, and risks 
outside the supply chain.

Table 1. Supply chain risks.
Authors Risk types
(Jüttner et al., 2003) Environmental, network-related, organizational risks
(Chopra & Sodhi, 2004) Disruptions, delays, information systems, forecast, intellectual property, procurement, 

receivables, inventory, and capacity risks
(Christopher & Peck, 2004) Internal risks: process and control risk, risks outside the firm but within the supply chain 

network: demand and supply risks, risks outside the supply chain: environmental risks
(Manuj & Mentzer, 2008) Supply, operational, demand, security, macro, political, competitive, and resource risks
(Wagner & Bode, 2008) Demand-side, supply-side, legal and regulatory, infrastructure, and catastrophic risks
(Trkman & McCormack, 
2009)

Endogenous (internal) risks: market and technology turbulence, Exogenous (external) 
risks; discrete events (e.g., terrorist attacks, contagious diseases, workers’ strikes, and 
continuous events (e.g., inflation rate, consumer price index changes)

(Kumar et al., 2010) Internal operational risks: demand, production and distribution, supply risks, External 
operational risks: terrorist attacks, natural disasters, exchange rate fluctuation

(Tummala & Schoenherr, 
2011)

Demand, delay, disruption, inventory, manufacturing (process) interruption, capacity, 
supply, system, sovereign, and transportation risks

(Samvedi et al., 2013) Supply, process, demand, and environmental risks
(Punniyamoorthy et al., 2013) Supply, manufacturing, demand, logistics, and environmental risks
(Ho et al., 2015) Macro risks, Micro risks: demand, manufacturing, supply, and infrastructure risks 

(information systems, transportation, and financial risks)
(Rangel et al., 2015) Planning: strategic, inertia, informational, capacity, and demand risks, Source: supply, 

financial and relational risks, Make: operational and disruption risks, Delivery: 
customer risk, Returns: legal risk, Other: environmental and cultural risks

(Prakash et al., 2017) Supply, demand, control, process, and environmental risks
(Shahbaz et al., 2019) Supply, process, demand, logistics, collaboration, financial, and environmental risks
(Chu et al., 2020) Political, environmental, financial, supply and demand, logistics, system, and 

operational risks
(Ali et al., 2021) Natural, human-made, system accidents, and financial
(Hermoso-Orzáez, & Garzón-
Moreno, 2021)

Operational, direct process to the product/service, suppliers, security, and labor rights

(Lahane & Kant, 2021) Operational and technological risks, product recovery risks, supply risks, demand risks, 
environmental risks, economic risks, social risks

(Parast & Subramanian, 2021) Supply risks, demand risks, process risks, and environmental risks
(Srivastava & Rogers, 2021) Operational, infrastructure, legal, economic, supplier, forecasting, transportation, and 

labor
(Duong et al., 2022) External risk, time risk, supply risk, operational risk, and demand risk
(Oturakçı & Yıldırım, 2022) Supply, manufacturing, demand, financial, macro, transportation, and information risks

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When the research on supply chain risks is reviewed, 
it has been observed that there is no universal 
classification. Classifications differ by industry or 
scope. As the content of this research is enormous, 
the number of risks was kept as wide as possible. 
Accordingly, environmental, safety, customer, 
supplier, transportation, and business risk groups 
were used in this article.

2.3. Supply chain risks assessment
Risk assessment is the second step of supply chain 
risk management. It is a critical step as the risk 
assessment affects the managerial decisions such 
as risk prioritization and resource allocation to 
mitigate risks (Sodhi & Tang, 2012; Hallikas et al., 
2004). There is a need to assess and prioritize risks 
to identify appropriate management actions for the 
risks at the focal business and supply chain levels. 
Since it will not be possible to take measures at the 
same level for all supply chain risks, it is necessary 
to determine each risk’s impact and rank them. Due 
to the reasons like budget, resources, time, labor 
constraints, etc., it is not possible to attach the 
same importance to all risks. Hence, it is a need to 
handle the risks with a proper approach (Oturakçı & 
Yıldırım, 2022). Ranking the risks helps managers 
focus on the risks that need immediate attention 
and choose the appropriate mitigation strategies. 
Various methods used in risk assessment are shown 
in Table 2.

A literature review for this study showed that the 
DEMATEL method is employed in some risk stud-
ies. However, to our best knowledge, there is no 
study using the DEMATEL to assess supply chain 
risks in the iron and steel industry. The DEMATEL 
method is accepted as one of the best tools for evalu-
ating the importance and causal relationships among 
evaluation criteria (Hsu et al., 2013).

3. Methodology

The fuzzy DEMATEL method and its methodological 
steps are explained in this section.

3.1. Fuzzy DEMATEL method
DEMATEL (Decision Making Trial and Evaluation 
Laboratory) is one of the multi-criteria decision-
making methods that helps to evaluate human 
judgments numerically. A better understanding of the 
causal relationship allows for planning and solving 
the problems by dividing the relevant factors into 

cause-effect groups. Also, there are no sample size 
limitations with the DEMATEL method (Govindan 
& Chaudhuri, 2016).

The main handicap in risk analysis is the subjectivity 
of the inputs obtained from the experts. Therefore, it 
is possible to use fuzzy or gray theories to minimize 
this subjectivity (Samvedi et al., 2013). Using fuzzy 
logic, experts can make inferences about the 
problems they encounter under uncertainty and 
quantitatively define the bilateral relations they 
evaluate with verbal expressions (Lin & Wu, 2008). 
Therefore, the DEMATEL method combining it with 
fuzzy theory is used in this study.

The steps of the fuzzy DEMATEL method are given 
below (Baykasoğlu et al., 2013):

Step 1: Constructing the fuzzy direct relationship 
matrix.

The relationships between the criteria are determined 
using the pairwise comparisons in Table 3. Here, a 
matrix is created with the answers obtained from 
each participant. The symbol z͂ij indicates the degree 
to which the i factor affects the j factor. In the formula 
Z͂k=[z͂kij] k refers to each participant and should be 
1≤k≤p Z͂1, Z͂2, Z͂3…Z͂p, participants show separate 

Table 2. Risk assessment methods.

Methods Authors
AHP (Hermoso-Orzáez, & Garzón-

Moreno, 2021)
AHP- Fuzzy TOPSIS (Alora & Barua, 2022)
ANP- Improved grey 
relational analysis

(Hashemi et al., 2015)

AHP- Fuzzy DEMATEL (Mzougui et al., 2020)
Bayesian networks (Lockamy III & McCormack, 

2009)
Fuzzy set theory- multi-
objective mathematical 
programming

(Ji & Zhu, 2012; Kumar 
et al., 2004)

Fuzzy-AHP (Samvedi et al., 2013)
Fuzzy-DEMATEL (Khan et al., 2021b)
Fuzzy-BWM (Khan et al., 2021a)
Fuzzy inference system- 
Bow-Tie analysis

(Aqlan & Lam, 2015)

DEMATEL -ANP (Tarei et al., 2018)
Newsvendor model (Cheong & Song, 2013)
Hybrid Petri-nets (Khilwani et al., 2011)
PF-AHP&PF-VIKOR (Lahane & Kant, 2021)
SEM-Fuzzy AHP (Oturakçı & Yıldırım, 2022)
Simulation (Durowoju et al., 2012)

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matrices of answers. If the number of participants is 
more than one, Z͂=[z͂ij] matrix is formed by calculating 
the arithmetic averages over the answers given to 
calculate the direct relationship matrix.

Table 3. The fuzzy linguistic scale.

Linguistic variables Triangular Fuzzy Numbers
No influence (No) (0, 0, 0.25)
Very low influence (VL) (0, 0.25, 0.50)
Low influence (L) (0.25, 0.50, 0.75)
High influence (H) (0.50, 0.75, 1)
Very high influence (VH) (0.75, 1, 1)

𝑍𝑍" =
�̃�𝑧! + �̃�𝑧" + ⋯+ �̃�𝑧#

𝑝𝑝
  (1)

𝑍𝑍" = $

0 �̃�𝑧!" ⋯ �̃�𝑧!#
�̃�𝑧"! 0 ⋯ �̃�𝑧"#
⋮ ⋮ ⋱ ⋮
�̃�𝑧#! �̃�𝑧#" ⋯ 0

+  (2)

Step 2: Calculating the normalized fuzzy direct 
relationship matrix

The ‘u’ value, the last of the triangular numbers, is 
used to create this matrix. The ‘r’ value obtained with 
the help of Equation (3) is divided by each value in 
the fuzzy direct relationship matrix.

𝑋𝑋" = $

𝑥𝑥&!! 𝑥𝑥&!" ⋯ 𝑥𝑥&!#
𝑥𝑥&"! 𝑥𝑥&"" ⋯ 𝑥𝑥&"#
⋮ ⋮ ⋱ ⋮
𝑥𝑥&#! 𝑥𝑥&#" ⋯ 𝑥𝑥&##

*   =	
$%!"
&
=

,
ℓ!"
&
,
(!"
&
,
)!"
&
.𝑟𝑟 = 𝑚𝑚𝑚𝑚𝑥𝑥!*+*#2∑ 𝑢𝑢+,

#
,-! 5   

 

ijx!

 (3)

Step 3: Calculating the fuzzy total relationship 
matrix.

In the normalized fuzzy direct relationship matrix, 
each triangular number group is divided into separate 
matrices, and using Equation (5) are combined into 
a single matrix to form a fuzzy total relationship 
matrix.

𝑋𝑋ℓ = #

0 ℓ"#$ ⋯ ℓ"%$
ℓ#"$ 0 ⋯ ℓ#%$
⋮ ⋮ ⋱ ⋮
ℓ%"$ ℓ%#$ ⋯ 0

) , 

𝑋𝑋& = #

0 𝑚𝑚"#$ ⋯ 𝑚𝑚"%$
𝑚𝑚#"$ 0 ⋯ 𝑚𝑚#%$
⋮ ⋮ ⋱ ⋮

𝑚𝑚%"$ 𝑚𝑚%#$ ⋯ 0

), 

𝑋𝑋' = #

0 𝑢𝑢"#$ ⋯ 𝑢𝑢"%$
𝑢𝑢#"$ 0 ⋯ 𝑢𝑢#%$
⋮ ⋮ ⋱ ⋮
𝑢𝑢%"$ 𝑢𝑢%#$ ⋯ 0

) 

 (4)

!ℓ!"
″ " = 𝑋𝑋ℓ(𝐼𝐼 − 𝑋𝑋ℓ)#$	

!𝑚𝑚!"
″ " = 𝑋𝑋%(𝐼𝐼 − 𝑋𝑋%)#$	

!𝑢𝑢!"
″ " = 𝑋𝑋&(𝐼𝐼 − 𝑋𝑋&)#$ 

𝑇𝑇- = .

�̃�𝑡$$ �̃�𝑡$' ⋯ �̃�𝑡$(
�̃�𝑡'$ �̃�𝑡'' ⋯ �̃�𝑡'(
⋮ ⋮ ⋱ ⋮
�̃�𝑡($ �̃�𝑡(' ⋯ �̃�𝑡((

4 

�̃�𝑡!" = (ℓ!"
) ,𝑚𝑚!"

) ,𝑢𝑢!"
) ) 

 

 (5)

Step 4: Defuzzification.

Different defuzzification methods are mentioned 
in the literature. In this study, the defuzzification 
method of CFCS (Converting Data into Crisp 
Values) proposed by Opricovic & Tzeng (2003) is 
used. Thus, triangular fuzzy numbers are converted 
to more net numbers.

R=maxj uij, L=minj ℓij  and     Δ=R−L  (6)

xℓj=(ℓij−L)/∆ (7)

xmj=(mij−L)/∆ (8)

xuj=(uij−L)/∆ (9)

xjls= xmj /(1+xmj−xℓj) (10)

xjrs= xuj /(1+xuj−xmj) (11)

xjcrisp =[xjls(1−xjls)+xjrsxjrs]/[1−xjls+xjrs] (12)

f͂ijcrisp =L+ xjcrisp∆ (13)

𝑇𝑇"!"# =

⎣
⎢
⎢
⎢
⎡�̃�𝑡$$

!"# �̃�𝑡$%
!"# ⋯ �̃�𝑡$&

!"#

�̃�𝑡%$
!"# �̃�𝑡%%

!"# ⋯ �̃�𝑡%&
!"#

⋮ ⋮ ⋱ ⋮
�̃�𝑡&$

!"# �̃�𝑡&%
!"# ⋯ �̃�𝑡&&

!"#⎦
⎥
⎥
⎥
⎤
     

�̃�𝑡'(
!"# = (ℓ'(

) ,𝑚𝑚'(
) ,𝑢𝑢'(

) )!"# 

 (14)

Step 5: Identifying cause and effect groups.

The “D͂def ” matrix is obtained by taking the sum of 
rows of defuzzified T͂def total relation matrix. The 
matrix “R͂def ” is obtained by transposing the matrix 
formed by the sum of the total relationship matrix 
columns, D͂idef shows the effects i. factor on other 
factors, R͂idef shows the sum of the direct and indirect 
effects on factor i. D͂idef+R͂idef shows the importance of 

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Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey

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the element of i in the whole system, and D͂idef−R͂idef 
shows the net effect of i criteria. If D͂idef−R͂idef is positive, 
it means it is a cause, and if negative, it is an effect.

Step 6: Calculating the threshold value and obtaining 
a cause and effect diagram.

In the T͂def total relationship matrix, a threshold value 
is determined to eliminate criteria with a relatively 
low degree of influence. An expert can determine this 
threshold value, or it can be obtained by summing 
the values in the total relationship matrix and 
averaging them. To easily understand the relations 
between the criteria and their positions relative 
to each other, an impact-relationship graph can be 
created that D͂idef+R͂idef is placed on the horizontal axis 
and D͂idef−R͂idef on the vertical axis.

4. A case study on the iron and steel 
industry in Turkey

Supply chain risks and their impact levels may 
differ depending on the industry characteristics of 
businesses. The iron and steel industry is of great 
importance for the overall performance of the 
manufacturing industry in Turkey due to its high 
rate of production and export potential and provision 
of inputs to other sectors (Kabak et al., 2016). In 
2019, the Turkish Steel Industry ranked 8th in the 
world and 2nd among steel producers in Europe after 
Germany (Iron Steel Sector Report, 2020). Due to its 
connection with many industries, disruptions in the 
iron and steel industry, directly and indirectly, affect 
other sectors. The iron and steel company chosen for 
this study is among the top 30 companies in Turkey’s 
Top 500 Industrial Enterprises. It has a corporate 
risk policy to identify and manage risks to increase 
its competitive advantage by reducing losses. The 
company’s senior management has adopted the 
vision of developing a practical risk management 
approach throughout the company and its suppliers.

A comprehensive literature review was made to 
identify main supply chain risks and sub-risks 
in this case study. This review resulted in a large 
number of supply chain risks. Some of these risks 
were eliminated based on the opinions of two 
academicians in the field of operations and supply 
chain management. Experts’ views at the executive 
level in the production, purchasing, and marketing 
departments were taken to determine the risks 
specific to the company’s supply chain. This process 
resulted in six risk groups and 58 risk types (Table 4).

Table 4. Identified supply chain risk group and sub-risks.

Risk Groups Risks

Environmental 
Risks (R1)

E1:  P Political uncertainty
E2:  PExchange rate
E3:  PRaw material price fluctuation
E4:  PBureaucracy
E5:  PEconomic crisis
E6:  PCompetition changes
E7:  PEnergy supply
E8:  PRegulatory

Security Risks 
(R2)

SC1:  PNatural disaster
SC2:  PTerrorism
SC3:  POccupational disease
SC4:  PWar
SC5:  PCyberattack
SC7:  POccupational accident

Customer 
Risks (R3)

C1:  PInsufficient information about 
customer orders or demand

C2:  POrder cancellation
C3:  PCustomers’ desire to expedite 

orders
C4:  PCustomers unable to make 

payments on time
C5:  PDemand uncertainty
C6:  PIncorrect information about 

customer orders or demand

Supplier Risks 
(R4)

S1:  PSupplier inability to deliver 
materials on time

S2:  PSupplier inability to provide 
material in desired quantity

S3:  PSupplier inability to provide 
materials of desired quality

S4:  PSupplier bankruptcy
S5:  PSupplier inability to respond to 

different types of material demand
S6:  PInability to select the right 

supplier
S7:  PFailure to share order information 

correctly with the supplier
S8:  PInability to fully share order 

information with the supplier
S9:  PInsufficient storage and handling
S10:  PLack of supplier capacity

Transportation 
Risks (R5)

T1:  PIncrease in transportation costs
T2:  PDelays due to railway capacity
T3:  PPort strikes
T4:  PDelays due to port capacity
T5:  PHigh transportation costs

Business 
Risks (R6)

B1:  PInsufficient or excess capacity
B2:  PInformation infrastructure 

breakdown
B3:  PInability to find qualified 

employees
B4:  PInsufficient or excess inventory

(Table 4 continues on next page)

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Risk Groups Risks

Business 
Risks (R6)

B5:  PCompany’s inability to meet 
demand changes 

B6:  PCompany’s inability to respond to 
different types of material demand

B7:  PMachine failure /production 
disruption

B8:  PLabor absenteeism
B9:  PHigh unit production cost
B10:  PCapacity cost
B11:  PManagement of labor strikes and 

union processes
B12:  PInability to deliver orders on 

time
B13:  PHigh labor turnover
B14:  PPoorly designed process
B15:  PInsufficient process 

improvements
B16:  PInability to retain qualified 

employee
B17:  PSecurity of critical information
B18:  PBusiness continuity disruption
B19:  PEnvironmental pollution
B20:  PInternal transportation and semi-

finished product/ finished product 
stocking

B21:  PCorporate communications
B22:  PFailure to make appropriate 

investments

Then, the experts made pairwise comparisons with 
linguistic expressions to determine the relationships 
between the six risk groups. The same linguistic 
comparison procedure was also performed for the 
sub-risks. Finally, risk groups and sub-risks were 
analyzed with the fuzzy DEMATEL method, and the 
results were evaluated.

After obtaining the pairwise comparison data on 
the risk groups, the following fuzzy DEMATEL 
methodology steps were followed.

Step 1: Establish the fuzzy direct relation matrix.

In Table 5, the binary linguistic comparisons of expert 
1 about the risk groups are shown as an example. For 
this purpose, expert 1 used the linguistic expressions 
given in Table 3.

Table 5. Linguistic assessment of risk groups of the 
expert 1.

R1 R2 R3 R4 R5 R6
R1 - L H L H VL
R2 L - L L H VL
R3 No VL - L No H
R4 VL No L - L H
R5 VL No H L - H
R6 L L VH H H -

The linguistic expressions obtained from the experts 
were converted into fuzzy triangular numbers. The 
fuzzy direct relationship matrix in Table 6 was 
obtained by taking the arithmetic average of the 
experts’ assessments.

Step 2: Calculate the normalized fuzzy direct 
relation matrix (Table 7).

Step 3: Calculate the total relation matrix (Table 8).

Step 4: Defuzzification.

CFCS (Converting Data into Crisp Values) 
defuzzification method proposed by Opricovic & 
Tzeng (6), (7), (8), (9), (10), (11), (12), (13), and (14) 
triangular numbers were converted into crisp values, 
and defuzzified total relation matrix was created 
(Table 9).

Step 5: Identify cause and effect groups.

D͂def and R͂def values, which give the row and column 
sums of the defuzzified total relation matrix, are 
calculated, and the importance level of risk groups, 
the cause-and-effect risk clusters are formed.

Step 6: Calculate the threshold value and draw the 
cause and effect diagram.

This study calculates the threshold value by taking 
the average values in the defuzzified fuzzy total 
relationship matrix, 0.440. In Table 9, threshold 
values and higher values are marked in bold.

4.1. Findings
Considering the analysis results of the risk groups 
in the study, the risk groups were divided into two 
clusters, as seen in Figure 1 and Table 10. These are 
the cause cluster with the positive D͂idef−R͂idef value 
and effect cluster with the negative D͂idef−R͂idef value.

Considering the D͂idef+R͂idef values, which show the 
degree of prominence of the criteria, it is seen that 
the highest valued risk group is business risks (R6) 
(5.945). This risk is followed by customer risks (R3) 
(5.529), supplier risks (R4) (5.211), transportation 
risks (R5) (5.206), environmental risks (R1) (5.077), 
and finally, security risks (R2) (4.716). Looking at 
the D͂idef−R͂idef values, the cause-and-effect clusters of 
risks emerge. R1 with the highest positive D͂idef−R͂idef 
value (0.830) possesses the most substantial effect 
on others, while R3 with the smallest negative value 
(−0.996) is the most influenced risk group.

(Table 4 continues on next page)

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The fuzzy DEMATEL steps applied for the risk groups 
were also carried out for the sub-risks, and a clustered 
values table was prepared to show D͂idef+R͂idef, D͂idef−R͂idef 
of the criteria and sorted according to these values.

When the environmental risks in Table 11 are 
examined, it can be seen that the most important 
environmental risk with the highest D͂idef+R͂idef value 
is the risks related to the economy (E5) (4.667). 
This risk is followed by E2 (4.172), E1 (4.135), E6 
(3.833). However, when the D͂idef−R͂idef values are 
examined, it is seen that E1 is the risk that most 
affects other risks with a value of 0.833. This risk 
was followed by E2 (0.383). Among the risks most 
influenced by the risks in this class, E6 (−0.845) took 
first place, while E3 (−0.429) took second place.

Looking at all interactions in the security risks in 
Table 12, it can be seen that war is the highest risk with 
D͂idef+R͂idef value 4.443 (SC4). Then, SC2 (4.383), SC5 
(4.229), SC6 (4.048), SC7 (3.588), SC1 (2.791), and 
SC3 (2.622) follow as well. Considering the D͂idef−R͂idef 
values, SC1 (1.030) is the risk most affects other risks, 
and SC6 (−0.076) is the most influenced risk.

Among the customer risks in Table 13, the risk of 
C1 (2.792) was determined as the most important 
risk. This risk was followed by C6 (2.537) and C2 
(2.446). Again, while C1 (0.505) had the strongest 
effect on other risks, C4 (−0.511) was the most 
influenced risk.

Table 9. The total relation matrix defuzzified with CFCS.

R1 R2 R3 R4 R5 C6 D͂idef D͂idef+R͂idef D͂idef–R͂idef

R1 0.313 0.415 0.595 0.537 0.527 0.566 2.953 5.077  0.830
R2 0.390 0.270 0.569 0.492 0.471 0.542 2.734 4.716  0.752
R3 0.327 0.296 0.369 0.398 0.399 0.477 2.267 5.529 –0.996
R4 0.330 0.312 0.528 0.336 0.435 0.504 2.445 5.211 –0.321
R5 0.340 0.291 0.577 0.480 0.333 0.539 2.560 5.206 –0.085
R6 0.423 0.398 0.625 0.523 0.480 0.434 2.883 5.945 –0.179
R͂idef 2.123 1.982 3.262 2.766 2.645 3.062

Table 6. Fuzzy direct relation matrix.

R1 R2 R3 R4 R5 R6
R1 0.000 0.000 0.000 0.333 0.583 0.833 0.375 0.625 0.875 0.417 0.667 0.917 0.458 0.708 0.958 0.375 0.625 0.792
R2 0.250 0.458 0.708 0.000 0.000 0.000 0.417 0.667 0.875 0.333 0.583 0.792 0.292 0.542 0.792 0.417 0.667 0.833
R3 0.208 0.375 0.625 0.125 0.292 0.542 0.000 0.000 0.000 0.208 0.375 0.625 0.292 0.458 0.708 0.417 0.667 0.875
R4 0.125 0.292 0.542 0.125 0.292 0.542 0.417 0.667 0.875 0.000 0.000 0.000 0.333 0.542 0.792 0.417 0.667 0.917
R5 0.125 0.292 0.542 0.042 0.125 0.375 0.625 0.875 1.000 0.375 0.667 0.833 0.000 0.000 0.000 0.542 0.792 0.958
R6 0.375 0.583 0.750 0.333 0.542 0.750 0.667 0.917 1.000 0.417 0.667 0.917 0.292 0.500 0.708 0.000 0.000 0.000

Table 7. Normalized fuzzy direct relation matrix.

R1 R2 R3 R4 R5 R6
R1 0.000 0.000 0.000 0.076 0.133 0.190 0.086 0.143 0.200 0.095 0.152 0.210 0.105 0.162 0.219 0.086 0.143 0.181
R2 0.057 0.105 0.162 0.000 0.000 0.000 0.095 0.152 0.200 0.076 0.133 0.181 0.067 0.124 0.181 0.095 0.152 0.190
R3 0.048 0.086 0.143 0.029 0.067 0.124 0.000 0.000 0.000 0.048 0.086 0.143 0.067 0.105 0.162 0.095 0.152 0.200
R4 0.029 0.067 0.124 0.029 0.067 0.124 0.095 0.152 0.200 0.000 0.000 0.000 0.076 0.124 0.181 0.095 0.152 0.210
R5 0.029 0.067 0.124 0.010 0.029 0.086 0.143 0.200 0.229 0.086 0.152 0.190 0.000 0.000 0.000 0.124 0.181 0.219
R6 0.086 0.133 0.171 0.076 0.124 0.171 0.152 0.210 0.229 0.095 0.152 0.210 0.067 0.114 0.162 0.000 0.000 0.000

Table 8. Total relation fuzzy matrix.

R1 R2 R3 R4 R5 R6
R1 0.034 0.163 1.028 0.099 0.265 1.145 0.154 0.414 1.595 0.140 0.361 1.448 0.145 0.353 1.418 0.144 0.388 1.518
R2 0.085 0.245 1.093 0.026 0.136 0.913 0.153 0.397 1.491 0.116 0.326 1.334 0.106 0.305 1.299 0.144 0.373 1.424
R3 0.069 0.196 0.953 0.048 0.169 0.904 0.052 0.207 1.149 0.081 0.242 1.151 0.095 0.246 1.133 0.130 0.319 1.264
R4 0.054 0.191 0.993 0.049 0.178 0.955 0.144 0.360 1.392 0.038 0.177 1.092 0.107 0.275 1.212 0.135 0.338 1.343
R5 0.059 0.203 0.998 0.036 0.157 0.931 0.195 0.418 1.419 0.123 0.326 1.258 0.042 0.180 1.064 0.168 0.380 1.357
R6 0.115 0.279 1.122 0.101 0.256 1.082 0.212 0.460 1.542 0.140 0.356 1.382 0.115 0.313 1.313 0.067 0.260 1.295

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As seen in Table 14, S1 with 𝐷𝑖𝑑𝑒𝑓+𝑅𝑖𝑑𝑒𝑓 value of 
7.940 is the first, and S2 (7.864) is the second most 
important risk in the supplier risks group. S8, with a 
value of 0.898, is the most affected, and S3 (−0.864) 
has emerged as the most influenced risk.

Among the transportation risks, T5 (8.840) was 
the most critical risk, while T2 took the last place 
in the order of importance. While T3 was the most 
affected transportation risk, T5 (−1.701) was the 
most influenced risk (Table 15).

Figure 1. The casual diagram of supply chain risks.

R1
R2

R3

R4

R5
R6

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

0 1 2 3 4 5 6 7D
 -R

D+R

Table 10. The rank of main risk groups.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 R6 5.945 1 R1 0.830

Cause Cluster
2 R3 5.529 2 R2 0.752
3 R4 5.211 3 R5 –0.085

Effect Cluster
4 R5 5.206 4 R6 –0.179
5 R1 5.077 5 R4 –0.321
6 R2 4.716 6 R3 –0.996

Table 11. Environmental risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 E5 4.667 1 E1 0.833

Cause Cluster
2 E2 4.172 2 E2 0.383
3 E1 4.135 3 E8 0.230
4 E6 3.833 4 E4 0.025
5 E3 3.745 5 E5 –0.012

Effect Cluster
6 E4 3.425 6 E7 –0.185
7 E7 3.391 7 E3 –0.429
8 E8 3.186 8 E6 –0.845

Table 12. The rank of security risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 SC4 4.443 1 SC1 1.030

Cause Cluster2 SC2 4.383 2 SC2 0.356
3 SC5 4.229 3 SC4 0.147
4 SC6 4.048 4 SC5 –0.076

Effect Cluster
5 SC7 3.588 5 SC3 –0.212
6 SC1 2.791 6 SC7 –0.585
7 SC3 2.622 7 SC6 –0.658

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Table 13. The rank of customer risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 C1 2.792 1 C1 0.505

Cause Cluster2 C6 2.537 2 C6 0.368
3 C2 2.446 3 C3 0.170
4 C5 2.036 4 C2 –0.220

Effect Cluster5 C3 1.979 5 C5 –0.312
6 C4 1.748 6 C4 –0.511

Table 15. The rank of transportations risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 T5 8.840 1 T3 0.960

Cause Cluster2 T1 8.699 2 T4 0.913
3 T3 7.478 3 T2 0.864
4 T4 7.428 4 T1 –1.037

Effect Cluster
5 T2 6.803 5 T5 –1.701

Table 16. The rank of business risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 B6 5.094 1 B11 0.995

Cause Cluster

2 B15 5.068 2 B16 0.960
3 B14 5.008 3 B21 0.762
4 B5 4.923 4 B8 0.683
5 B22 4.618 5 B17 0.555
6 B10 4.568 6 B2 0.487
7 B1 4.567 7 B7 0.339
8 B4 4.507 8 B3 0.278
9 B12 4.503 9 B15 0.256
10 B18 4.464 10 B13 0.222
11 B3 4.360 11 B14 0.081
12 B9 4.325 12 B22 –0.005

Effect Cluster

13 B16 4.197 13 B19 –0.189
14 B8 4.160 14 B10 –0.262
15 B7 4.151 15 B1 –0.317
16 B20 4.140 16 B18 –0.415
17 B13 4.075 17 B6 –0.466
18 B21 3.903 18 B20 –0.567
19 B11 3.854 19 B4 –0.692
20 B2 3.606 20 B5 –0.698
21 B17 3.024 21 B9 –0.861
22 B19 2.864 22 B12 –1.145

Table 14. The rank of supplier risks.

Rank Risks D͂idef+R͂idef Rank Risks D͂idef–R͂idef Cluster
1 S1 7.940 1 S8 0.898

Cause Cluster
2 S2 7.864 2 S7 0.805
3 S5 7.344 3 S10 0.641
4 S6 7.339 4 S6 0.384
5 S3 7.098 5 S4 0.035
6 S4 6.601 6 S5 –0.433

Effect Cluster
7 S8 6.443 7 S1 –0.445
8 S7 6.397 8 S2 –0.494
9 S10 6.380 9 S9 –0.526
10 S9 5.758 10 S3 –0.864

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Finally, when we look at the business risks in 
Table 15, according to the value, B6 (5.094) is in the 
first place, while B15 (5.068) is the second. and B14 
(5.008) is the third most significant risk. According 
to values, B11 (0.995), one of the most affected 
business risks, is first, while B16 (0.960) is second. 
B12 (−1.145) ranks first in the influenced business 
risk, and B9 (−0.861) ranks second.

5. Conclusions and discussions

Identifying and assessing risks in supply chains is very 
important to eliminate or reduce the consequences 
of risks. It would be insufficient to address the 
risks in a supply chain one by one and assess their 
individual effects. Evaluating each risk individually 
and with other risks enables managers to obtain more 
accurate and meaningful results. This study aims to 
determine the supply chain risk groups and sub-risks 
of a leading company operating in Turkey’s iron and 
steel industry and examine the causal relationships 
between the risks. For this purpose, the pairwise 
comparison data about the risks obtained from the 
experts in the company’s purchasing, production, 
and marketing departments were analyzed with the 
fuzzy DEMATEL method.

Results show that the most crucial risk group is the 
business risks. This is followed by customer risks, 
supplier risks, transportation risks, environmental 
risks, and, finally, security risks. Business risks, 
also called operational risks, arise in a company’s 
internal product development, manufacturing, and 
distribution operations (Deloitte, 2012). Companies 
invest in programs such as Total Quality Management 
(TQM), Lean Manufacturing, and Six Sigma to 
improve their quality and capabilities. However, 
these programs reduce the tolerance of faults 
and increase the negative effect of any problems 
(Punniyamoorthy et al., 2013). Therefore, it is not 
surprising that business risk is first in the study. 
Also, it can be said that business operations take the 
most part for a greater supply chain performance 
(Duong et al., 2022). The results showed that the 
environmental risk is the most influential of the 
other risks. This risk group has a low probability of 
occurring but a high impact. Therefore, risks in this 
category drastically cause many disruptions in the 
supply chain (Alora & Barua, 2022). Kumar et al. 
(2010) evaluated environmental risks as interaction 
risks and stated that the supply chain environment 
emerges from interaction with physical, social, legal, 
operational, economic, and political factors.

Similarly, Samvedi et al. (2013) stated that 
environmental risks could affect a single level 
or organization or the entire supply chain. In our 
study, customer risk is most affected by the other 
risk groups. One of the supply chain management 
purposes is to achieve customer satisfaction by 
meeting customer needs and expectations. Therefore, 
any disruption at any stage of the supply chain will 
directly or indirectly affect customer satisfaction.

When the results of environmental risks are 
examined, it is observed that economic risk is the 
most critical risk. Similarly, Kumar et al. (2020) 
assessed various risks affecting demand for the 
Indian automotive sector. They found that companies 
are most affected by economic risks than other risks. 
Political uncertainty affects the other environmental 
risks the most. This could be because those political 
uncertainties in Turkey harm the economy. Political 
tensions can also affect companies within the 
country and, therefore, supply chain partners in other 
countries (Mostafa et al., 2021). Environmental risk 
analysis also indicated that competition risk is the 
most affected environmental risk type.

Regarding the security risks, it has been seen that the 
two most crucial security risks are terrorism and war 
risk. A current example of war risk is the Ukraine-
Russia war. Ukraine exports components such as 
iron ores, ferro-silico manganese, and pig iron that 
are input to the European steel industry. Due to the 
war, there is a potential for supply chain disruption 
in the European iron and steel industry (World Bank, 
2022). A natural disaster is a risk that triggers other 
environmental risks. Chopra & Sodhi (2004) stated 
that natural disasters, which they consider one of 
the unpredictable and rare disruptions, interrupt the 
physical flow in the supply chain.

When the importance of customer risks is assessed, 
insufficient information about customer orders/
demand and incorrect information about customer 
orders/ demand come first and second, respectively. 
The analysis emphasizes the importance of demand-
side risks since the lack of demand information is the 
most influential on other types of customer risks. To 
reduce the demand-side supply chain risks, decision-
makers should identify the factors that increase 
the demand risks and make appropriate process 
improvements to reduce them.

Regarding the supplier risks, the most important 
is that the company’s suppliers cannot deliver the 
materials in the desired time and quantity. In the 

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literature, some authors (e.g., Kumar et al., 2010; 
Punniyamoorthy et al., 2013) indicated that supplier-
related risks would negatively affect the ability of the 
focal firm to meet customer demand (both in terms of 
quantity and quality) at the anticipated costs and at 
the desired time. Therefore, it would be beneficial for 
company managers to develop reactive and proactive 
action plans against supplier-related risks.

Among the transportation risks, high transportation 
costs and an increase in transportation costs take 
the first and second places in the company. Experts 
consider transportation costs as an essential risk 
that threatens the company. As disruptions in 
transportation operations prevent the timely supply 
of materials, it can disrupt the company’s production 
activities (Paul et al., 2020). So, transportation is 
seen as one of the critical risks (Schoen et al., 2018). 
Transport mode, which depends on the final products’ 
characteristics, is a strategic variable that increases 
supply chain performance (Oliveira et al., 2017). 
Maritime transportation is very important in this 
company. Therefore, port strikes were found to have 
the most effect on other transportation risks. On the 
other hand, it was determined that high transportation 
costs were influenced by the other transportation risks 
the most. According to this finding, it can be said that 
the company should reduce other transportation risks 
to reduce high transportation costs.

Regarding the business risks, the first risk is the 
inability to respond to different types of material 
demand. The second risk is insufficient process 
improvements. And the third risk is poorly 
designed processes. These results indicate that 
there are deficiencies in the company’s own internal 
operational processes. For this reason, the managers 
should identify inefficient business processes and 
make necessary process improvements. The most 
affected business risk is that orders cannot be 
delivered on time, proving that internal problems are 
reflected on the customer.

This study is comprehensive as it includes many 
supply chain risks. The case study presented sets an 
example for practitioners and researchers to identify 
supply chain risks and assess the impact on each 
other. However, the lack of evaluation of experts 
from other companies in the iron and steel industry 
limits the generalization of the research findings. 
Similar studies in different sectors can be conducted 
to enrich the field of supply chain risk management. 
In addition, studies that include risk mitigation 
strategies, which is the third stage of supply chain 
risk management, will provide significant benefits 
to academicians and managers working in supply 
chain risk management. Finally, this study only used 
the fuzzy DEMATEL method. In the future, hybrid 
techniques may be used for model creation.

References
Ali, S.M., Paul, S.K., Chowdhury, P., Agarwal, R., Fathollahi-Fard, A.M., Jabbour, C.J.C., & Luthra, S. (2021). Modelling 

of supply chain disruption analytics using an integrated approach: An emerging economy example. Expert Systems with 
Applications, 173, 114690. https://doi.org/10.1016/j.eswa.2021.114690

Alora, A., & Barua, M.K. (2022). Development of a supply chain risk index for manufacturing supply chains. International 
Journal of Productivity and Performance Management. 71(2), 477-503. https://doi.org/10.1108/IJPPM-11-2018-0422

Aqlan, F., & Lam, S.S. (2015). A fuzzy-based integrated framework for supply chain risk assessment. International Journal 
of Production Economics, 161, 54-63. https://doi.org/10.1016/j.ijpe.2014.11.013

Baykasoğlu, A., Kaplanoğlu, V., Durmuşoğlu, Z.D.U., & Şahin, C. (2013). Integrating fuzzy DEMATEL and 
fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications, 40(3), 899-907. 
https://doi.org/10.1016/j.eswa.2012.05.046

Can Saglam, Y., Yildiz Çankaya, S., & Sezen, B. (2020). Proactive risk mitigation strategies and supply chain risk 
management performance: an empirical analysis for manufacturing firms in Turkey. Journal of Manufacturing 
Technology Management. 32(6), 1234-1244. https://doi.org/10.1108/JMTM-08-2019-0299

Ceryno, P.S., Scavarda, L.F., & Klingebiel, K. (2015). Supply chain risk: Empirical research in the automotive industry. 
Journal of Risk Research, 18(9), 1145-1164. https://doi.org/10.1080/13669877.2014.913662

Cheong, T., & Song, S.H. (2013). The value of information on supply risk under random yields. Transportation Research 
Part E: Logistics and Transportation Review, 60, 27-38. https://doi.org/10.1016/j.tre.2013.09.006

Chopra, S., & Sodhi, M.M.S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 
46(1), 53-61.

Christopher, M., & Peck, H. (2004). Building the Resilient Supply Chain. The International Journal of Logistics 
Management, 15(2), 1-14. https://doi.org/10.1108/09574090410700275

Int. J. Prod. Manag. Eng. (2022) 10(2), 195-209 Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Üstündağ et al.

206

https://doi.org/10.1016/j.eswa.2021.114690
https://doi.org/10.1108/IJPPM-11-2018-0422
https://doi.org/10.1016/j.ijpe.2014.11.013
https://doi.org/10.1016/j.eswa.2012.05.046
https://doi.org/10.1108/JMTM-08-2019-0299
https://doi.org/10.1080/13669877.2014.913662
https://doi.org/10.1016/j.tre.2013.09.006
https://doi.org/10.1108/09574090410700275
http://creativecommons.org/licenses/by-nc-nd/4.0/


Chu, C.Y., Park, K., & Kremer, G.E. (2020). A global supply chain risk management framework: An application 
of text-mining to identify region-specific supply chain risks. Advanced Engineering Informatics, 45, 101053. 
https://doi.org/10.1016/j.aei.2020.101053

Deloitte. (2012). Supply Chain Resilience: A Risk Intelligent approach to managing global supply chains, https://www2.
deloitte.com/global/en/pages/governance-risk-and-compliance/articles/risk-intelligent-approach-managing-supply-
chains.html

Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International 
Journal of Production Economics, 182, 144-156. https://doi.org/10.1016/j.ijpe.2016.08.021

Duong, A.T.B., Vo, V.X., Carvalho, M.D.S., Sampaio, P., & Truong, H.Q. (2022). Risks and supply chain performance: 
globalization and COVID-19 perspectives. International Journal of Productivity and Performance Management. 
https://doi.org/10.1108/IJPPM-03-2021-0179

Durowoju, O.A., Chan, H.K., & Wang, X. (2012). Entropy assessment of supply chain disruption. Journal of Manufacturing 
Technology Management, 23(8), 998-1014. https://doi.org/10.1108/17410381211276844

Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A 
DEMATEL based approach. Transportation Research Part E: Logistics and Transportation Review, 90, 177-195. 
https://doi.org/10.1016/j.tre.2015.11.010

Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16. https://doi.org/10.3390/
risks9010016

Hachicha, W., & Elmsalmi, M. (2014). An integrated approach based-structural modeling for risk prioritization in supply 
network management. Journal of Risk Research, 17(10), 1301-1324. https://doi.org/10.1080/13669877.2013.841734

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M., & Tuominen, M. (2004). Risk management processes in supplier 
networks. International Journal of Production Economics, 90(1), 47-58. https://doi.org/10.1016/j.ijpe.2004.02.007

Hashemi, S.H., Karimi, A., & Tavana, M. (2015). An integrated green supplier selection approach with analytic network 
process and improved Grey relational analysis. International Journal of Production Economics, 159, 178-191. https://
doi.org/10.1016/j.ijpe.2014.09.027

Hermoso-Orzáez, M.J., & Garzón-Moreno, J. (2021). Risk management methodology in the supply chain: a case study 
applied. Annals of Operations Research, 1-25. https://doi.org/10.1007/s10479-022-04583-w

Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International 
Journal of Production Research, 53(16), 5031-5069. https://doi.org/10.1080/00207543.2015.1030467

Hopkin, P. (2018). Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk 
Management (5th Edition). Kogan Page.

Hsu, C.-W., Kuo, T.-C., Chen, S.-H., & Hu, A.H. (2013). Using DEMATEL to develop a carbon management 
model of supplier selection in green supply chain management. Journal of Cleaner Production, 56, 164-172. 
https://doi.org/10.1016/j.jclepro.2011.09.012

Iron Steel Sector Report. (2020). https://www.sanayi.gov.tr/assets/pdf/plan-program/DemirÇelikSektörRaporu2020.pdf (In 
Turkish)

Ji, G., & Zhu, C. (2012). A study on emergency supply chain and risk based on urgent relief service in disasters. Systems 
Engineering Procedia, 5, 313-325. https://doi.org/10.1016/j.sepro.2012.04.049

Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda 
for future research. International Journal of Logistics Research and Applications, 6(4), 197-210. 
https://doi.org/10.1080/13675560310001627016

Kabak, Ö., Ülengin, F., Çekyay, B., Önsel, Ş., & Özaydın, Ö. (2016). Critical success factors for the Iron and Steel 
Industry in Turkey: A Fuzzy DEMATEL Approach. International Journal of Fuzzy Systems, 18(3), 523-536. 
https://doi.org/10.1007/s40815-015-0067-7

Khan, S., Haleem, A., & Khan, M.I. (2021a). Assessment of risk in the management of Halal supply chain using fuzzy 
BWM method. Supply Chain Forum: An International Journal, 22(1), 57-73. https://doi.org/10.1080/16258312.2020. 
1788905

Khan, S., Haleem, A., & Khan, M.I. (2021b). Risk management in Halal supply chain: an integrated fuzzy Delphi and 
DEMATEL approach. Journal of Modelling in Management, 16(1), 172-214. https://doi.org/10.1108/JM2-09-2019-
0228

Khilwani, N., Tiwari, M.K., & Sabuncuoglu, I. (2011). Hybrid Petri-nets for modelling and performance evaluation of 
supply chains. International Journal of Production Research, 49(15), 4627-4656. https://doi.org/10.1080/00207543.
2010.497173

Int. J. Prod. Manag. Eng. (2022) 10(2), 195-209Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey

207

https://doi.org/10.1016/j.aei.2020.101053
https://www2.deloitte.com/global/en/pages/governance-risk-and-compliance/articles/risk-intelligent-approach-managing-supply-chains.html
https://www2.deloitte.com/global/en/pages/governance-risk-and-compliance/articles/risk-intelligent-approach-managing-supply-chains.html
https://www2.deloitte.com/global/en/pages/governance-risk-and-compliance/articles/risk-intelligent-approach-managing-supply-chains.html
https://doi.org/10.1016/j.ijpe.2016.08.021
https://doi.org/10.1108/IJPPM-03-2021-0179
https://doi.org/10.1108/17410381211276844
https://doi.org/10.1016/j.tre.2015.11.010
https://doi.org/10.3390/risks9010016
https://doi.org/10.3390/risks9010016
https://doi.org/10.1080/13669877.2013.841734
https://doi.org/10.1016/j.ijpe.2004.02.007
https://doi.org/10.1016/j.ijpe.2014.09.027
https://doi.org/10.1016/j.ijpe.2014.09.027
https://doi.org/10.1007/s10479-022-04583-w
https://doi.org/10.1080/00207543.2015.1030467
https://doi.org/10.1016/j.jclepro.2011.09.012
https://www.sanayi.gov.tr/assets/pdf/plan-program/DemirÇelikSektörRaporu2020.pdf
https://doi.org/10.1016/j.sepro.2012.04.049
https://doi.org/10.1080/13675560310001627016
https://doi.org/10.1007/s40815-015-0067-7
https://doi.org/10.1080/16258312.2020.1788905
https://doi.org/10.1080/16258312.2020.1788905
https://doi.org/10.1108/JM2-09-2019-0228
https://doi.org/10.1108/JM2-09-2019-0228
https://doi.org/10.1080/00207543.2010.497173
https://doi.org/10.1080/00207543.2010.497173
http://creativecommons.org/licenses/by-nc-nd/4.0/


Kumar, G., Singh, R.K., Jain, R., & Kain, R. (2020). Analysis of demand risks for the Indian automotive sector in globally 
competitive environment. International Journal of Organizational Analysis, 30(4), 836-863. https://doi.org/10.1108/
IJOA-03-2020-2076

Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply 
chain. Computers and Industrial Engineering, 46(1), 69-85. https://doi.org/10.1016/j.cie.2003.09.010

Kumar, S.K., Tiwari, M.K., & Babiceanu, R.F. (2010). Minimisation of supply chain cost with embedded risk using 
computational intelligence approaches. International Journal of Production Research, 48(13), 3717-3739. 
https://doi.org/10.1080/00207540902893425

Lahane, S., & Kant, R. (2021). Evaluation and ranking of solutions to mitigate circular supply chain risks. Sustainable 
Production and Consumption, 27, 753-773. https://doi.org/10.1016/j.spc.2021.01.034

Lin, C.J., & Wu, W.W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert 
Systems with Applications, 34(1), 205-213. https://doi.org/10.1016/j.eswa.2006.08.012

Lockamy III, A., & McCormack, K. (2009). Examining Operational Risks in Supply Chains. Supply Chain Forum: An 
International Journal, 10(1), 2-14. https://doi.org/10.1080/16258312.2009.11517204

Manuj, I., & Mentzer, J.T. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133-155. 
https://doi.org/10.1002/j.2158-1592.2008.tb00072.x

Mital, M., Del Giudice, M., & Papa, A. (2018). Comparing supply chain risks for multiple product categories with 
cognitive mapping and Analytic Hierarchy Process. Technological Forecasting and Social Change, 131, 159-170. 
https://doi.org/10.1016/j.techfore.2017.05.036

Mostafa, A.I., Rashed, A.M., Alsherif, Y.A., Enien, Y.N., Kaoud, M., & Mohib, A. (2021, October). Supply Chain Risk 
Assessment Using Fuzzy Logic. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) 
(pp. 246-251). IEEE. https://doi.org/10.1109/NILES53778.2021.9600100

Munir, M., Jajja, M.S.S., Chatha, K.A., & Farooq, S. (2020). Supply chain risk management and operational performance: 
The enabling role of supply chain integration. International Journal of Production Economics, 227, 107667. 
https://doi.org/10.1016/j.ijpe.2020.107667

Mzougui, I., Carpitella, S., Certa, A., El Felsoufi, Z., & Izquierdo, J. (2020). Assessing supply chain risks in the automotive 
industry through a modified MCDM-Based FMECA. Processes, 8(5), 579. https://doi.org/10.3390/pr8050579

Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. 
International Journal of Production Economics, 118(1), 168-174. https://doi.org/10.1016/j.ijpe.2008.08.045

Oliveira, F.L., Junior, A.D.R.O., & Rebelo, L.M.B. (2017). Adapting transport modes to supply chains classified by 
the uncertainty supply chain model: A case study at Manaus Industrial Pole. International Journal of Production 
Management and Engineering, 5(1), 39-43. https://doi.org/10.4995/ijpme.2017.5775

Opricovic, S., & Tzeng, G.H. (2003). Defuzzification within a multi-criteria decision model. International Journal of 
Uncertainty, Fuzziness and Knowlege-Based Systems, 11(5), 635-652. https://doi.org/10.1142/S0218488503002387

Oturakçı, M., & Yıldırım, R.S. (2022). Analysis of supply chain risks by structural equation model and fuzzy analytical 
hierarchy process. Pamukkale University Journal of Engineering Sciences, 28(1), 117-127. https://doi.org/10.5505/
pajes.2021.34119

Parast, M.M., & Subramanian, N. (2021). An examination of the effect of supply chain disruption risk drivers on 
organizational performance: evidence from Chinese supply chains. Supply Chain Management: An International 
Journal, 26(4), 548-562. https://doi.org/10.1108/SCM-07-2020-0313

Paul, S., Kabir, G., Ali, S.M., & Zhang, G. (2020). Examining transportation disruption risk in supply chains: A case 
study from Bangladeshi pharmaceutical industry. Research in Transportation Business & Management, 37, 100485. 
https://doi.org/10.1016/j.rtbm.2020.100485

Pfohl, H.C., Gallus, P., & Thomas, D. (2011). Interpretive structural modeling of supply chain risks. International Journal 
of Physical Distribution and Logistics Management, 41(9), 839-859. https://doi.org/10.1108/09600031111175816

Prakash, S., Soni, G., & Rathore, A.P.S. (2017). A critical analysis of supply chain risk management content: a structured 
literature review. Journal of Advances in Management Research, 14(1), 69-90. https://doi.org/10.1108/JAMR-10-2015-
0073

Pujawan, I.N., & Bah, A.U. (2022). Supply chains under COVID-19 disruptions: literature review and research agenda. 
Supply Chain Forum: An International Journal, 23(1), 81-95. https://doi.org/10.1080/16258312.2021.1932568

Punniyamoorthy, M., Thamaraiselvan, N., & Manikandan, L. (2013). Assessment of supply chain risk: Scale development 
and validation. Benchmarking, 20(1), 79-105. https://doi.org/10.1108/14635771311299506

Rajesh, R., & Ravi, V. (2017). Analyzing drivers of risks in electronic supply chains: a grey–DEMATEL approach. 
International Journal of Advanced Manufacturing Technology, 92(1-4), 1127-1145. https://doi.org/10.1007/s00170-
017-0118-3

Int. J. Prod. Manag. Eng. (2022) 10(2), 195-209 Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Üstündağ et al.

208

https://doi.org/10.1108/IJOA-03-2020-2076
https://doi.org/10.1108/IJOA-03-2020-2076
https://doi.org/10.1016/j.cie.2003.09.010
https://doi.org/10.1080/00207540902893425
https://doi.org/10.1016/j.spc.2021.01.034
https://doi.org/10.1016/j.eswa.2006.08.012
https://doi.org/10.1080/16258312.2009.11517204
https://doi.org/10.1002/j.2158-1592.2008.tb00072.x
https://doi.org/10.1016/j.techfore.2017.05.036
https://doi.org/10.1109/NILES53778.2021.9600100
https://doi.org/10.1016/j.ijpe.2020.107667
https://doi.org/10.3390/pr8050579
https://doi.org/10.1016/j.ijpe.2008.08.045
https://doi.org/10.4995/ijpme.2017.5775
https://doi.org/10.1142/S0218488503002387
https://doi.org/10.5505/pajes.2021.34119
https://doi.org/10.5505/pajes.2021.34119
https://doi.org/10.1108/SCM-07-2020-0313
https://doi.org/10.1016/j.rtbm.2020.100485
https://doi.org/10.1108/09600031111175816
https://doi.org/10.1108/JAMR-10-2015-0073
https://doi.org/10.1108/JAMR-10-2015-0073
https://doi.org/10.1080/16258312.2021.1932568
https://doi.org/10.1108/14635771311299506
https://doi.org/10.1007/s00170-017-0118-3
https://doi.org/10.1007/s00170-017-0118-3
http://creativecommons.org/licenses/by-nc-nd/4.0/


Rangel, D.A., De Oliveira, T.K., & Leite, M.S.A. (2015). Supply chain risk classification: Discussion and proposal. 
International Journal of Production Research, 53(22), 6868-6887. https://doi.org/10.1080/00207543.2014.910620

Samvedi, A., Jain, V., & Chan, F.T.S. (2013). Quantifying risks in a supply chain through integration of fuzzy AHP and 
fuzzy TOPSIS. International Journal of Production Research, 51(8), 2433-2442. https://doi.org/10.1080/00207543.2
012.741330

Schoen, Q., Sanchis, R., Poler, R., Lauras, M., Fontanili, F., & Truptil, S. (2018). Categorisation of the main disruptive 
events in the sensitive products transportation supply chains. International Journal of Production Management and 
Engineering, 6(2), 79-89. https://doi.org/10.4995/ijpme.2018.10369

Shahbaz, M.S., RM Rasi, R.Z., & Bin Ahmad, M.F. (2019). A novel classification of supply chain risks: Scale development 
and validation. Journal of Industrial Engineering and Management, 12(1), 201. https://doi.org/10.3926/jiem.2792

Sharma, S., & Routroy, S. (2016). Modeling information risk in supply chain using Bayesian networks. Journal of 
Enterprise Information Management, 29(2), 238-254. https://doi.org/10.1108/JEIM-03-2014-0031

Sodhi, M.S., & Tang, C.S. (2012). Managing Supply Chain Risk. In Springer Science & Business Media. https://doi.
org/10.1007/978-1-4614-3238-8.

Sreedevi, R., Saranga, H., & Gouda, S.K. (2021). Impact of a country’s logistical capabilities on supply chain risk. Supply 
Chain Management: An International Journal, https://doi.org/10.1108/SCM-09-2020-0504.

Srivastava, M., & Rogers, H. (2021). Managing global supply chain risks: effects of the industry sector. International 
Journal of Logistics Research and Applications, 1-24.

Tarei, P.K., Thakkar, J.J., & Nag, B. (2018). A hybrid approach for quantifying supply chain risk and prioritizing the risk 
drivers: A case of Indian petroleum supply chain. Journal of Manufacturing Technology Management, 29(3), 533-569. 
https://doi.org/10.1108/JMTM-10-2017-0218 

Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments-A conceptual model for managing 
supply chain network risk. International Journal of Production Economics, 119(2), 247-258. https://doi.org/10.1016/j.
ijpe.2009.03.002

Tukamuhabwa, B.R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review 
and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623. 
https://doi.org/10.1080/00207543.2015.1037934

Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the Supply Chain Risk Management Process 
(SCRMP). Supply Chain Management, 16(6), 474-483. https://doi.org/10.1108/13598541111171165

Venkatesh, V.G., Rathi, S., & Patwa, S. (2015). Analysis on supply chain risks in Indian apparel retail chains and proposal 
of risk prioritization model using Interpretive structural modeling. Journal of Retailing and Consumer Services, 26, 
153-167. https://doi.org/10.1016/j.jretconser.2015.06.001

Wagner, S.M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. 
Journal of Business Logistics, 29(1), 307-325. https://doi.org/10.1002/j.2158-1592.2008.tb00081.x

World Bank. 2022. The Impact of the War in Ukraine on Global Trade and Investment. Washington, DC. World Bank. 
https://openknowledge.worldbank.org/handle/10986/37359, License: CC BY 3.0 IGO

Zimmer, K., Fröhling, M., Breun, P., & Schultmann, F. (2017). Assessing social risks of global supply chains: A quantitative 
analytical approach and its application to supplier selection in the German automotive industry. Journal of Cleaner 
Production, 149, 96-109. https://doi.org/10.1016/j.jclepro.2017.02.041

Int. J. Prod. Manag. Eng. (2022) 10(2), 195-209Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey

209

https://doi.org/10.1080/00207543.2014.910620
https://doi.org/10.1080/00207543.2012.741330
https://doi.org/10.1080/00207543.2012.741330
https://doi.org/10.4995/ijpme.2018.10369
https://doi.org/10.3926/jiem.2792
https://doi.org/10.1108/JEIM-03-2014-0031
https://doi.org/10.1007/978-1-4614-3238-8
https://doi.org/10.1007/978-1-4614-3238-8
https://doi.org/10.1108/SCM-09-2020-0504
https://doi.org/10.1108/JMTM-10-2017-0218
https://doi.org/10.1016/j.ijpe.2009.03.002
https://doi.org/10.1016/j.ijpe.2009.03.002
https://doi.org/10.1080/00207543.2015.1037934
https://doi.org/10.1108/13598541111171165
https://doi.org/10.1016/j.jretconser.2015.06.001
https://doi.org/10.1002/j.2158-1592.2008.tb00081.x
https://openknowledge.worldbank.org/handle/10986/37359
https://doi.org/10.1016/j.jclepro.2017.02.041
http://creativecommons.org/licenses/by-nc-nd/4.0/