Fuzzy MCDM Framework for Risk Management in Construction
Supply Chain
Abdullah Ali Salamai
a
Management Department, Applied College, Jazan University, Jazan, Saudi Arabia, K.S.A.
Keywords: Risk Management, Construction Supply Chain, Fuzzy Sets, Multi-Criteria Decision Making, Supply Chain
Management, Artificial Intelligence, Blockchain.
Abstract: Risk management in the construction supply chain (CSC) is vital in construction project risks. CSC has
various risks affecting product quality and project timeline, such as operational, social, financial, technical,
design, and safety risks. These risks should be mitigated in project construction. So, this paper proposed a set
of technologies to overcome risks in CSC, like artificial intelligence (AI), blockchain, data analytics, and IoT,
to select the best one. So, the multi-criteria decision-making (MCDM) methodology is used to deal with
various risks. The Multi-Attribute Utility Theory (MAUT) method is used to rank technologies. The weights
of risks are obtained by the average method by using the decision matrix. The MCDM methodology is
integrated with a fuzzy set to overcome uncertainty data. Experts used triangular fuzzy numbers to express
their opinions instead of exact numbers. These allow the model to overcome inconsistent and vague data. The
MCDM methodology was applied to 18 risks and 5 technologies. The results show that social risks have the
highest weight. AI is the best technology for overcoming risks in CSC. AI can integrate with CSC from raw
data to final products to deliver to the usert.
1 INTRODUCTION
Supply chain management (SCM) controls the
production flow of products and services from raw
materials to the final product to deliver goods to
clients. Firms and companies use various suppliers to
deliver projects, from raw materials to final products
and users. The role of SCM is to reduce the time of
the production cycle and reduce cost. The
effectiveness of the SCM maximizes the value of the
supply chain. Various criteria are performed to
increase the effectiveness of SCM, like identifying
potential issues, optimizing price dynamically, and
enhancing inventory allocation (Hmouda et al., 2024;
Oyewole et al., 2024).
SCM was extended with various applications and
case studies in healthcare, medical, retail, suppliers
section(Sa’diyah et al., 2022), service companies, and
food industries. Construction plays a vital role in the
global marketplace. It can aid countries in creating
opportunities for skilled and unskilled labor.
A construction project refers to using energy and
raw materials, products, and hybrid nature. The
a
https://orcid.org/0000-0001-9679-1545
quality of construction projects is increased by the
performance of the project team and customer
satisfaction with products.
Construction supply chain (CSC) refers to the
process of a series of tasks from raw data to final
goods in the construction industry. CSC is the process
of sourcing, purchasing, and delivering materials. It
is a network of suppliers that provide raw data into a
final product to the user. It includes the flow of
produce from suppliers to the construction site. It
plays a vital role in the cost, time of projects, and
quality of projects. CSC has various risks that affect
the quality and performance of the system. These
risks include cultural risks, social risks, financial
risks, technical risks, and design risks. Various
technologies are used with CSC to reduce these risks,
like artificial intelligence (AI), blockchain, IoT, and
data analysis (Chen et al., 2024; Gharaibeh et al.,
2024).
AI is the common solution for addressing and
minimizing risks in CSC. Each part in CSC forms raw
data and the final product is managed by AI. The role
of AI in CSC can optimize productivity and reduce
146
Salamai, A. A.
Fuzzy MCDM Framework for Risk Management in Construction Supply Chain.
DOI: 10.5220/0013135700003956
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business (FEMIB 2025), pages 146-153
ISBN: 978-989-758-748-1; ISSN: 2184-5891
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
the effect of labor storage. AI can use the historical
data of products and aid companies in predicting
operational resources. AI can implement proactive
maintenance methods and strategies. AI can analyze
the unstructured data (Pournader et al., 2021; Singh et
al., 2023).
Blockchain aids companies and firms in CSC by
knowing the SC network, where the accumulation
and exchange of value happen through a set of
transactions, services, products, and information.
Every business can add value for good before
reaching the final step. Blockchain is interfaced with
other technologies such as IoT and AI to deliver
sustainable, secured, and safe CSC (Hijazi et al.,
2019; Yoon & Pishdad-Bozorgi, 2022).
In decision-making, the experts and decision-
makers are complex, and it is difficult to express their
opinions in exact numbers in multi-criteria decision-
making (MCDM) systems. So, the fuzzy set was
applied to deal with vague data. The fuzzy set was
used in various decision-making issues. So, the fuzzy
set is a suitable framework for enabling decision-
makers to express their opinions using v, uncertain
data instead of exact numbers. Triangular fuzzy
numbers (TFNs) are fuzzy sets that deal with vague
data(Dong et al., 2021; Dubois et al., 2004). The
MCDM methods are applied in various decision-
making issues like renewable energy sources(Ali &
Muthuswamy, 2023), green fuels evaluation(Elsayed,
2024), wastewater treatment(Saeed et al., 2024), and
energy solar(Alharbi et al., 2024).
The Multi-Attribute Utility Theory (MAUT)
approach is an MCDM methodology. The main
advantage of the MAUT method is its simplicity in
solving various criteria in decision-making problems.
It can offer abundant freedom of action experts to
make outcomes more effective and accurate. This
method is applied in decision-making issues to select
the best option. This method belongs to compensatory
approaches; factors are independent, and qualitative
factors are converted into quantitative ones(de Freitas
et al., 2013; Işık, 2017).
1.1 Risk Management
Construction is quick and is a vital element in the
supply chain. Delivering raw data from suppliers to
sites is essential for a timely project. The main
challenges of construction are sourcing and
procurement of materials. There are various
categories of materials in the supply chain
(Shishehgarkhaneh et al., 2024; Yu & Ma, 2024).
There are steps in risk management to reduce risks in
CSC:
Identify and evaluate the potential risks in SC
materials. It makes the SC more comprehensive in
supply chain management. The project manager can
investigate their vendor network to reduce risks.
Identifying the risks can reduce the time of the project
and deliver products and services on time.
Applying risk mitigation methods and strategies
to reduce complex timelines in construction projects.
The construction projects have risk mitigation
strategies to deliver projects on time.
1.2 Contributions of this Study
The primary contributions of this work are:
• This work presents the risk management for the
construction supply chain. We introduced the
risks of CSC and how to reduce these risks.
• We introduce some trend technologies to
overcome CSC risks. We introduce five trend
technologies to select the best one.
• We used the MCDM concept to manage
multiple risks in CSC and the MCDM method
to select the best technology.
• We used a triangular fuzzy set to deal with
vague data in the selection problem. This study
uses five main technologies and 18 CSC risks.
• We show that AI is the best technology to
reduce CSC risks by analyzing the historical
data and predicting the demand of supply to
overcome risks.
1.3 Organization of this Paper
The rest of this paper is organized as follows: Section
2 shows the previous studies in CSC for risk
management. Section 3 shows the materials and
methods of this study; we introduce the MCDM
methodology with the fuzzy set to deal with vague
data. Section 4 shows the results and discussion of
this study. Section 5 shows the sensitivity analysis.
Section 6 shows the conclusions of this work.
2 LITERATURE REVIEW
Risk management plays an important role in CSC for
effective performance and operation with uncertainty
degrees. Various models and frameworks are used to
reduce risks in CSC, like supply risks and risks of
SCM. Pham et al. (Pham et al., 2023) aimed to reduce
and overcome risks in CSC. They presented a
complete review to show different risks and how to
Fuzzy MCDM Framework for Risk Management in Construction Supply Chain
147
reduce them in CSC. They focused on risk
management for the CSC process and operation.
Shojaei and Haeri (Shojaei & Haeri, 2019)
proposed a framework to reduce risks in CSC. They
used fuzzy cognitive mapping and gray relational
analysis. They applied their model in real cases to
show the performance and effectiveness of their
model. They evaluated various risks by their model.
They applied their model to reduce complexity and
risks in the construction process, avoid time and cost,
and project failure.
Tah and Carr (Tah & Carr, 2001) defined the
limitations in risk management for CSC tools, and
methods. They used the methods for describing risks
for the development stable knowledge-driven method
for risk management. They defined the generic risk
and remedial action in descriptive terms. They
implemented their model in the data management
system. They adopted a prototype system to support
risk management in CSC.
Aloini et al. (Aloini et al., 2012) proposed work to
analyze the CSC with various factors in the
construction industry. They provided a complete
review of risk management in CSC. They provided
case studies and tests to show the limitations results
of CSC.
Hernadewita and Saleh (Hernadewita & Saleh,
2020) enhanced tools and approaches for risk
management and evaluation in CSC. They used the
literature review methodology to find tools and
methods, including AHP, FMEA, SCOR, and
HAZOP. They show the limitations and advantages
of defining and evaluating CSC for risk management.
Abas et al. (Abas et al., 2022) aimed to identify
the risks and factors impacting CSC. They adopted a
methodology for identifying risk and success criteria.
They created questionnaires to collect the opinions of
firms and project managers. They show the highest
risk in CSC financials, followed by storage materials,
cash flow, and bad weather. Their study shows the
enhancement of the construction industry.
Senthil and Muthukannan (Senthil &
Muthukannan, 2022) introduced a complete survey to
focus on quality management and quality assurance
processes in the construction industry. They reviewed
the risk management for CSC and showed that a
neural network depends on a network by weight
training input.
Rudolf and Spinler (Rudolf & Spinler, 2018)
introduced a ranked view on the CSC for risk
management. They provide a contextualized risk for
engineering and construction projects. They showed
the highest risk is inherent risk in large-scale projects
and behavior risks.
Obayi et al. (Obayi & Ebrahimi, 2021) provided a
study to show the role of external pressures in risk
management in CSC. They showed a case study of
regulatory strategies in CSC. They showed that
relational costs have the highest weight, followed by
transportation costs.
Deng et al. (Deng et al., 2019) presented a
framework to analyze the CSC risks. They used the
fuzzy synthetic evaluation to evaluate the risks in
CSC. They presented nine risks with high weight and
discussed the criteria risks in CSC.
3 MATERIAL AND METHODS
MCDM methods are used in decision-making issues
to make the best decision. This section shows the
steps of the MCDM framework under TFNs to select
the best technology in CSC risk management (Dong
et al., 2021; Işık,, 2017). Figure 1 shows the MCDM
framework. Also, we show some definition of TFNs
as:
Definition 1
We introduce some definition of triangular fuzzy
numbers (TFNs) as:
TFNs defined as: 𝑏=
𝑏
,𝑏
,𝑏
is a fuzzy set
defined on the set R of real numbers whose
membership is:
𝑧
𝑥
=
⎩
⎪
⎨
⎪
⎧
, 𝑖𝑓 𝑏
≤𝑥≤ 𝑏
, 𝑖𝑓 𝑏
≤𝑥≤ 𝑏
0, 𝑖𝑓 𝑥>𝑏
𝑜𝑟 𝑥<𝑏
(1)
Where 𝑏
,𝑏
,𝑏
define as a low, mode, and
upper bound of TFNs.
Definition 2
We can compute the graded mean integration
representation:
𝑅
𝑏
=
𝑏
+4𝑏
+𝑏
(2)
Definition 3
The fuzzy weights vector of TFNs can be defined
as:
∑
𝑤
=1,𝑤
+
∑
𝑤
,
≤1,𝑤
+
∑
𝑤
,
≥1 (3)
Step 1. Data collection
The step invited the experts to evaluate the criteria
and alternatives. This study invited 5 experts with
high experience. These experts have more than 20
years of experience in supply construction chain
management.
FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business
148
Step 2. Build assessment matrix
The assessment matrix is built between factors
and options by using the options of experts. The
experts used the linguistic terms of triangular fuzzy
sets. Then we used the triangular fuzzy numbers to
build the assessment matrix. Then we convert these
numbers into crisp numbers.
Step 3. Combine the assessment matrix.
The assessment matrix is combined by using the
average method to attain one matrix.
Step 4. Compute the factors' weights.
The experts evaluated the factors and options.
Then we used the average method to combine these
factors to attain factor weights.
Step 5. Normalize the assessment matrix.
The assessment matrix is normalized by using the
beneficial and non-beneficial factors such as:
𝑥
=
;𝑖=1,…,𝑚;𝑗=1,…,𝑛 (4)
𝑥
=1+
;𝑖=1,…,𝑚;𝑗=
1,…,𝑛 (5)
Where 𝑞
refers to the value in the assessment
matrix.
Step 6. Compute the marginal utility score
𝑦
=
.
;𝑖=1,…,𝑚;𝑗=1,…,𝑛 (6)
Step 7. Computing the final utility score
𝑅
=
∑
𝑤
𝑦
;𝑖=1,…,𝑚 (7)
Step 8. Rank the alternatives.
The final utility score is ranked descending to
obtain the final rank of options.
Figure 1: The steps of MCDM methodology.
Figure 2: List of factors and technologies.
4 RESULTS AND DISCUSSION
This section shows the results of the MCDM
framework for selecting the best technology to reduce
risks in CSC through risk management. This study
used the MCDM method to rank alternatives. The
fuzzy set is used to overcome vague data through
evaluation steps.
CSC is the process used to control the flow of
sources and materials in the construction area. CSC
has various components and steps, such as project
management, logistic operations, manufacturing
elements, and raw materials procurement. CSC aims
to preserve and maintain strong relations between
manufacturers and suppliers. The best SCM with
cost-effective products delivered on time and project-
building effectiveness. However, several risks the
CSC faces affect its process, performance, and
effectiveness. CSC has various risks and challenges,
such as multiple fragmented processes, long
production times, balancing inventory levels, legal
risks, safety risks, environmental risks, financial
risks, and culture risks.
Risks in CSC can lead to a complex SC process
and bad quality products and performance.
Construction firms must select technology to aid them
in the SC process, complete their projects on time,
and preserve a competitive edge in the construction
industry. AI can overcome and reduce the risks in
CSC. AI aids construction firms in reducing safety
risks, operational risks, and costs. AI can be used
throughout the CSC process, from planning to the
final steps. AI has various models and algorithms that
can analyze large amounts of data from multiple
sources to show predictive results. These models can
solve the risk of prediction delays. AI models can aid
in the preservation of business continuity. AI models
can analyze and train large amounts of data, like
historical project data, customer needs, and market
Fuzzy MCDM Framework for Risk Management in Construction Supply Chain
149
trends, to predict accurate demand predictions.
Construction firms can use the demand prediction
results to predict the upcoming materials and data to
overcome the risks.
AI models and algorithms can aid a firm’s
construction to assess suppliers with some factors like
time of delivery, dependability, and quality of goods.
AI models can evaluate the performance of each
supplier in CSC and select the best one. This can
increase the performance and effectiveness of each
supplier in CSC. AI models and algorithms can
reduce risks in CSC by empowering firms to design
risk mitigation methods and strategies. Firms can use
AI models for early detection of risks to mitigate the
effect of one project's time and cost.
Step 1. Criteria are collected from previous
studies based on CSC risks, and five main
technologies are used to select the best one to reduce
risks in CSC. Figure 2 shows the risks and
technologies for CSC. This study invited five experts
to assess the factors and technologies. These experts
used the linguistic terms of triangular fuzzy sets.
Step 2. The assessment matrix is built between
factors and technologies using the TFNs. Then, these
numbers are converted to crisp numbers as shown in
Table 3.
Step 3. The assessment matrix combines factors
and technologies to obtain a single matrix with TFNs.
Step 4. The factor weights are obtained by using
the average method. Figure 3 shows the factor's
weights.
From the weights results, we show that social
risks are the most important, with a weight of
0.066922, followed by discontinuity of supply risks,
with a weight of 0.066922, Transportation Risks, with
a weight of 0.06266; Financial Risks, with a weight
of 0.061807, Scattered Supplier Risks, with a weight
of 0.060102, and Logistics Risks, with a weight of
0.058397.
We show the management strategies' efficacy has
the lowest importance with a weight of 0.041347,
followed by Cultural Risks with a weight of
0.042199, followed by safety risks with a weight of
0.04902, Operational Risks with a weight of
0.050725, and Timeline Deviations Risks with a
weight 0.05243.
Step 5. Eq. (4) is used to normalize the decision
matrix between factors and technologies as shown in
Table 1.
Step 6. Eq. (6) is used to compute the marginal
utility score as shown in Table 2.
Step 7. Eq. (7) is used to compute the final utility
score as shown in Figure 4.
Step 8. Technologies are ranked based on the
highest value in the final utility score. We show that
AI has the highest rank followed by IoT, Blockchain,
data analytics, and quantitative models.
Figure 3: Factors weights.
Figure 4: Final utility score values for each technology.
Table 1: The Normalized Decision Matrix.
CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
0.875 0 1 0.375 0.25
CNC
2
1 0.5 0.083333 0 0.416667
CNC
3
0 0.526316 0.631579 0.315789 1
CNC
4
0.3 0.9 0.6 0 1
CNC
5
0.416667 0.833333 1 0.333333 0
CNC
6
1 0 0.5 0.9 0.3
CNC
7
1 0.5625 0.9375 0.625 0
CNC
8
1 0 0.375 1 0.875
CNC
9
0 1 0.666667 0.75 0.916667
CNC
10
0.181818 1 0.181818 0 1
CNC
11
0 0 0.545455 0.636364 1
CNC
12
0 0.166667 0.833333 0.666667 1
CNC
13
1 0.25 0 1 1
CNC
14
1 0.625 0 0.125 0.875
CNC
15
0.777778 1 0.333333 0 0.666667
CNC
16
1 0 0.636364 0.545455 0.272727
CNC
17
0 1 0.8 1 0.8
CNC
18
0 0.375 0.6875 1 0.875
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0
0.5
1
1.5
2
2.5
3
CNT1 CNT2 CNT3 CNT4 CNT5
FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business
150
Table 2: The marginal utility score.
CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
3.365265 0.584795 4.321085 1.238012 0.964164
CNC
2
4.321085 1.589638 0.690854 0.584795 1.3456
CNC
3
0.584795 1.675545 2.068171 1.099753 4.321085
CNC
4
1.065567 3.537806 1.941589 0.584795 4.321085
CNC
5
1.3456 3.096193 4.321085 1.139026 0.584795
CNC
6
4.321085 0.584795 1.589638 3.537806 1.065567
CNC
7
4.321085 1.801296 3.813345 2.041136 0.584795
CNC
8
4.321085 0.584795 1.238012 4.321085 3.365265
CNC
9
0.584795 4.321085 2.218519 2.620871 3.65772
CNC
10
0.841258 4.321085 0.841258 0.584795 4.321085
CNC
11
0.584795 0.584795 1.740924 2.088057 4.321085
CNC
12
0.584795 0.816148 3.096193 2.218519 4.321085
CNC
13
4.321085 0.964164 0.584795 4.321085 4.321085
CNC
14
4.321085 2.041136 0.584795 0.750892 3.365265
CNC
15
2.770595 4.321085 1.139026 0.584795 2.218519
CNC
16
4.321085 0.584795 2.088057 1.740924 1.009001
CNC
17
0.584795 4.321085 2.89651 4.321085 2.89651
CNC
18
0.584795 1.238012 2.31291 4.321085 3.365265
5 SENSITIVITY ANALYSIS
We conducted a sensitivity analysis to ensure the
validity of the proposed model by showing the rank
of alternatives under different situations. We
proposed nineteen situations of criteria weights, as
shown in Figure 5. In the first situation, all criteria
were given equal weight. Then, in the second
situation, the first criterion was given 0.1 weight, and
all criteria had the same weight.
Then, we show the rank of alternatives under
different situations, as shown in Figure 6. We show
that alternative 5 is the best in all situations. So, the
rank of other options is stable under different
situations.
Figure 5. The different situations of criteria weights.
Figure 6: The rank of alternatives under different situations.
6 CONCLUSIONS
This study proposed an MCDM model for reducing
risks in CSC using the risk management framework.
This study used the MCDM method, and MAUT was
used to rank options. The average method using the
decision matrix obtains the factors' weights. Five
experts with experience in CSC and risk management
evaluated the factors and technologies. There are 18
risks, and 5 technologies were used in this study. The
assessment matrix is built between factors and 5
technologies. The triangular fuzzy numbers are used
to evaluate the factors and options. Then, these
numbers are converted to the crisp number. Then, we
combined this matrix into one matrix. The criteria
weights are obtained. The results show that social risk
has the highest weight. The MAUT is applied to rank
the options. The results show that AI has the highest
rank. AI can aid CSC by reducing the risks by
predicting historical data to show the best demand in
the future to deliver products on time.
The limitations of this paper are a few criteria and
alternatives. So, in future work, we will maximize the
number of criteria and alternatives. Another
limitation is the number of experts, in future study,
the number of experts will increase.
Various MCDM methods, such as AHP, BWM,
and DEMATEL, will be used to obtain the factor's
weight in future studies. The 5 technologies can be
extended to include multiple technologies to reduce
risks in CSC.
0.00
0.02
0.04
0.06
0.08
0.10
CNC1
CNC2
CNC3
CNC4
CNC5
CNC6
CNC7
CNC8
CNC9
CNC…
CNC…
CNC…
CNC…
CNC…
CNC…
CNC…
CNC…
CNC…
S1 S2 S3 S4 S5
S6 S7 S8 S9 S10
S11 S12 S13 S14 S15
S16 S17 S18 S19
0
1
2
3
4
5
6
CNT1 CNT2 CNT3 CNT4 CNT5
S1 S2 S3 S4 S5 S6 S7
S8 S9 S10 S11 S12 S13 S14
S15 S16 S17 S18 S19
Fuzzy MCDM Framework for Risk Management in Construction Supply Chain
151
Table 3: The assessment matrix between factors and
technologies.
CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
(
5,7,9
)
(
3,5,7
)
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
CNC
2
(7,9,11) (1,1,1) (1,3,5) (1,1,1) (3,5,7)
CNC
3
(
1,1,1
)
(
1,3,5
)
(
7,9,11
)
(
1,1,1
)
(
7,9,11
)
CNC
4
(1,3,5) (5,7,9) (7,9,11) (1,3,5) (5,7,9)
CNC
5
(
3,5,7
)
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
6
(5,7,9) (1,1,1) (5,7,9) (7,9,11) (1,3,5)
CNC
7
(
7,9,11
)
(
1,3,5
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
CNC
8
(7,9,11) (3,5,7) (7,9,11) (5,7,9) (5,7,9)
CNC
9
(
1,1,1
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
10
(1,3,5) (7,9,11) (1,3,5) (1,3,5) (7,9,11)
CNC
11
(
1,3,5
)
(
1,1,1
)
(
5,7,9
)
(
1,1,1
)
(
7,9,11
)
CNC
12
(
3,5,7
)
(
1,3,5
)
(
7,9,11
)
(
7,9,11
)
(
5,7,9
)
CNC
13
(
5,7,9
)
(
3,5,7
)
(
1,1,1
)
(
5,7,9
)
(
7,9,11
)
CNC
14
(
7,9,11
)
(
5,7,9
)
(
1,3,5
)
(
3,5,7
)
(
5,7,9
)
CNC
15
(
5,7,9
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
(
3,5,7
)
CNC
16
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
1,1,1
)
(
1,3,5
)
CNC
17
(
1,1,1
)
(
1,3,5
)
(
3,5,7
)
(
5,7,9
)
(
1,1,1
)
CNC
19
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
Second ex
p
ert CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
(
5,7,9
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
CNC
2
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,1,1
)
(
3,5,7
)
CNC
3
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
7,9,11
)
CNC
4
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
CNC
5
(
3,5,7
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
CNC
6
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
7
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
CNC
8
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
CNC
9
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
CNC
10
(
1,3,5
)
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
11
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
7,9,11
)
CNC
12
(
3,5,7
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
CNC
13
(
1,3,5
)
(
5,7,9
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
14
(
7,9,11
)
(
5,7,9
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
15
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
(
3,5,7
)
CNC
16
(
5,7,9
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
CNC
17
(
1,1,1
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
18
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
Third Ex
p
ert CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
(
5,7,9
)
(
3,5,7
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
CNC
2
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
(
3,5,7
)
CNC
3
(
1,1,1
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
7,9,11
)
CNC
4
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
5
(3,5,7) (7,9,11) (7,9,11) (7,9,11) (1,1,1)
CNC
6
(
5,7,9
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
CNC
7
(7,9,11) (7,9,11) (7,9,11) (1,1,1) (1,1,1)
CNC
8
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
5,7,9
)
(
5,7,9
)
CNC
9
(1,1,1) (5,7,9) (7,9,11) (7,9,11) (1,1,1)
CNC
10
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
1,3,5
)
(
7,9,11
)
CNC
11
(1,3,5) (1,1,1) (7,9,11) (7,9,11) (1,1,1)
CNC
12
(
3,5,7
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
5,7,9
)
CNC
13
(5,7,9) (3,5,7) (7,9,11) (7,9,11) (1,1,1)
CNC
14
(
7,9,11
)
(
7,9,11
)
(
7,9,11
)
(
1,1,1
)
(
5,7,9
)
CNC
15
(5,7,9) (7,9,11) (7,9,11) (1,1,1) (3,5,7)
CNC
16
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
1,1,1
)
(
1,3,5
)
CNC
17
(1,1,1) (7,9,11) (7,9,11) (1,1,1) (1,1,1)
CNC
18
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
Fourth ex
p
ert CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
(
5,7,9
)
(
3,5,7
)
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
CNC
2
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
3,5,7
)
CNC
3
(
1,1,1
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
7,9,11
)
CNC
4
(
1,3,5
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
5
(
3,5,7
)
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
6
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
7,9,11
)
(
1,3,5
)
CNC
7
(
7,9,11
)
(
1,3,5
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
8
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
(
5,7,9
)
CNC
9
(
1,1,1
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
10
(
1,3,5
)
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
11
(
1,3,5
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
7,9,11
)
CNC
12
(
3,5,7
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
13
(
5,7,9
)
(
3,5,7
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
14
(
7,9,11
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
CNC
15
(5,7,9) (5,7,9) (1,1,1) (1,3,5) (3,5,7)
CNC
16
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
1,1,1
)
(
1,3,5
)
CNC
17
(1,1,1) (5,7,9) (1,1,1) (1,3,5) (1,1,1)
CNC
18
(
1,1,1
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
(
5,7,9
)
Fifth expert CNT
1
CNT
2
CNT
3
CNT
4
CNT
5
CNC
1
(
5,7,9
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
5,7,9
)
CNC
2
(7,9,11) (1,3,5) (1,3,5) (5,7,9) (3,5,7)
CNC
3
(
1,1,1
)
(
1,3,5
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
CNC
4
(1,3,5) (1,3,5) (5,7,9) (1,3,5) (5,7,9)
CNC
5
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
1,1,1
)
(
1,3,5
)
CNC
6
(5,7,9) (1,3,5) (1,3,5) (5,7,9) (1,3,5)
CNC
7
(
7,9,11
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
1,1,1
)
CNC
8
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
5,7,9
)
(
5,7,9
)
CNC
9
(
1,1,1
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
5,7,9
)
CNC
10
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
1,3,5
)
(
7,9,11
)
CNC
11
(
1,3,5
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
12
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
CNC
13
(
5,7,9
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
14
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
CNC
15
(
5,7,9
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
16
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
1,3,5
)
CNC
17
(
1,1,1
)
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
CNC
18
(
1,3,5
)
(
1,3,5
)
(
5,7,9
)
(
7,9,11
)
(
5,7,9
)
REFERENCES
Abas, M., Khattak, S. B., Habib, T., & Nadir, U. (2022).
Assessment of critical risk and success factors in
construction supply chain: a case of Pakistan.
International Journal of Construction Management,
22(12), 2258–2266.
Alharbi, A. H., Jabali, H. M., Rajan, P., & Salamai, A. A.
(2024). Evaluation Challenges of Leadership
Management in the Energy Sector using Multi-Criteria
Decision Making Approach. Multicriteria Algorithms
with Applications, 4, 16–27.
Ali, A. M., & Muthuswamy, M. (2023). Neutrosophic
Multi-Criteria Decision-Making Framework for
Sustainable Evaluation of Power Production Systems in
Renewable Energy Sources. Sustainable Machine
Intelligence Journal, 4, 1–3.
Aloini, D., Dulmin, R., Mininno, V., & Ponticelli, S.
(2012). Supply chain management: a review of
implementation risks in the construction industry.
Business Process Management Journal, 18(5), 735–
761.
Chen, Z., Hammad, A. W. A., & Alyami, M. (2024).
Building construction supply chain resilience under
supply and demand uncertainties. Automation in
Construction, 158, 105190.
de Freitas, L. V., de Freitas, A., Veraszto, E. V., Marins, F.
A. S., & Silva, M. B. (2013). Decision-making with
multiple criteria using AHP and MAUT: An industrial
application. Eur. Int. J. Sci. Technol, 2(9), 93–100.
Deng, X., Yang, X., Zhang, Y., Li, Y., & Lu, Z. (2019).
Risk propagation mechanisms and risk management
strategies for a sustainable perishable products supply
chain. Computers & Industrial Engineering, 135,
1175–1187.
Dong, J., Wan, S., & Chen, S.-M. (2021). Fuzzy best-worst
method based on triangular fuzzy numbers for multi-
FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business
152
criteria decision-making. Information Sciences, 547,
1080–1104.
Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004).
Probability-possibility transformations, triangular
fuzzy sets, and probabilistic inequalities. Reliable
Computing, 10(4), 273–297.
Elsayed, A. (2024). Multi-Criteria Decision-Making
Framework for Evaluating Green Fuels Alternatives: A
Hybrid MEREC-TODIM Approach. Neutrosophic
Optimization and Intelligent Systems, 3, 41–56.
Gharaibeh, L., Eriksson, K. M., Lantz, B., Matarneh, S., &
Elghaish, F. (2024). Toward digital construction supply
chain-based Industry 4.0 solutions: scientometric-
thematic analysis. Smart and Sustainable Built
Environment, 13(1), 42–62.
Hernadewita, H., & Saleh, B. I. (2020). Identifying tools
and methods for risk identification and assessment in
construction supply chain. International Journal of
Engineering, 33(7), 1311–1320.
Hijazi, A. A., Perera, S., Alashwal, A., & Calheiros, R. N.
(2019). Blockchain adoption in construction supply
chain: A review of studies across multiple sectors. CIB
World Building Congress, Hong Kong, 2021.
Hmouda, A. M. O., Orzes, G., & Sauer, P. C. (2024).
Sustainable supply chain management in energy
production: A literature review. Renewable and
Sustainable Energy Reviews, 191, 114085.
Işık, A.T. (2017). Critic and Maut Methods for the Contract
Manufacturer Selection Problem. European Journal of
Multidisciplinary Studies Articles, 2.
Obayi, R., & Ebrahimi, S. N. (2021). A neo-institutional
view of the transaction cost drivers of construction
supply chain risk management. Supply Chain
Management: An International Journal, 26(5), 592–
609.
Oyewole, A. T., Okoye, C. C., Ofodile, O. C., & Ejairu, E.
(2024). Reviewing predictive analytics in supply chain
management: Applications and benefits. World Journal
of Advanced Research and Reviews, 21(3), 568–574.
Pham, H. T., Pham, T., Truong Quang, H., & Dang, C. N.
(2023). Supply chain risk management research in
construction: a systematic review. International
Journal of Construction Management, 23(11), 1945–
1955.
Pournader, M., Ghaderi, H., Hassanzadegan, A., &
Fahimnia, B. (2021). Artificial intelligence applications
in supply chain management. International Journal of
Production Economics, 241, 108250.
Rudolf, C. A., & Spinler, S. (2018). Key risks in the supply
chain of large scale engineering and construction
projects. Supply Chain Management: An International
Journal, 23(4), 336–350.
Sa’diyah, N. K., Enggarsasi, U., & Prameswari, S. (2022).
Automatic Transportation System and Human
Resources Development on Society Era 5.0 in
Indonesia. ICoSTE 2020, 111–116.
https://doi.org/10.5220/0010797000003317
Saeed, M., Kareem, K., Razzaq, F., & Saqlain, M. (2024).
Unveiling Efficiency: Investigating Distance Measures
in Wastewater Treatment Using Interval-Valued
Neutrosophic Fuzzy Soft Set. Neutrosophic Systems
with Applications, 15, 1–15.
Senthil, J., & Muthukannan, M. (2022). Development of
lean construction supply chain risk management based
on enhanced neural network. Materials Today:
Proceedings, 56, 1752–1757.
Shishehgarkhaneh, M. B., Moehler, R. C., Fang, Y., Hijazi,
A. A., & Aboutorab, H. (2024). Transformer-Based
Named Entity Recognition in Construction Supply
Chain Risk Management in Australia. IEEE Access, 12,
41829–41851.
Shojaei, P., & Haeri, S. A. S. (2019). Development of
supply chain risk management approaches for
construction projects: A grounded theory approach.
Computers & Industrial Engineering, 128, 837–850.
Singh, A., Dwivedi, A., Agrawal, D., & Singh, D. (2023).
Identifying issues in adoption of AI practices in
construction supply chains: towards managing
sustainability. Operations Management Research,
16(4), 1667–1683.
Tah, J. H. M., & Carr, V. (2001). Towards a framework for
project risk knowledge management in the construction
supply chain. Advances in Engineering Software,
32(10–11), 835–846.
Yoon, J. H., & Pishdad-Bozorgi, P. (2022). State-of-the-art
review of blockchain-enabled construction supply
chain. Journal of Construction Engineering and
Management, 148(2), 3121008.
Yu, R., & Ma, L. (2024). Risk evaluation of mega
infrastructure construction supply chain in engineering-
procurement-construction projects: an integrated fuzzy
AHP and fuzzy DEMATEL approach. Engineering,
Construction and Architectural Management.
Fuzzy MCDM Framework for Risk Management in Construction Supply Chain
153
