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Automation in Construction 162 (2024) 105396
Available online 20 March 2024
0926-5805/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Review
Construction supply chain risk management
Milad Baghalzadeh Shishehgarkhaneh
a
, Robert C. Moehler
a
,
b
,
*
, Yihai Fang
a
,
Hamed Aboutorab
c
, Amer A. Hijazi
d
a
Department of Civil Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3800, Australia
b
Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Australia
c
School of Business, UNSW Canberra, Canberra, ACT, Australia
d
Department of Civil Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
ARTICLE INFO
Keywords:
Construction supply chain management
Risk management
Articial intelligence
Systematic literature review
Bibliometric analysis
ABSTRACT
Risk management in construction projects requires effective construction supply chain risk management
(CSCRM). To gain insights into CSCRM research, this paper conducts a systematic literature review and bib-
liometric analysis covering the period from 1999 to 2023. The ndings of this comprehensive analysis shed light
on various aspects, including risk management phases, classication of micro or macrolevel risks, traditional
approaches, and the emergence of articial intelligence (AI) applications. Through an extensive database search,
relevant articles on CSCRM were identied for analysis. The review reveals that while traditional techniques such
as surveys, case studies, and statistical tools remain prominent, there is an increasing adoption of AI methods.
Initially focused on risk identication, assessment, and analysis; the CSCRM phases have expanded over time to
include risk allocation, prioritization, and recovery. Analysis of publication trends shows a rise in the use of AI
techniques since 2016 alongside persistent utilization of traditional approaches. Moreover, inuential authors,
journals, and collaborative networks are highlighted to provide valuable insights into the eld’s development.
Overall visualization contributes to advancing both research and practice in CSCRM by presenting a holistic
overview of theories, methods, and emerging technologies within the eld along with critical risk management
approaches and publication trends.
1. Introduction
Supply Chain Management (SCM) originated in the manufacturing
industry in the 1980s and it represented a paradigm shift in the way
companies approached their logistics and materials management [1],
and it was used to improve the efciency and effectiveness of the supply
chain. Toyota has been at the forefront of implementing the Just in Time
(JIT) concept in their delivery system [2,3]. They focus on ensuring the
appropriate quantity of supplies is available at the precise moment it is
needed. By doing so, they minimize inventory and optimize the coor-
dination between suppliers and the production line [4].
Over time, the concept of SCM has expanded beyond the
manufacturing industry to include other sectors such as retail [5],
healthcare [6], food industry [7], and service industries [8]. Today, SCM
is considered a critical business strategy for organizations to remain
competitive in the global marketplace. Construction industry plays a
pivotal role in driving the global economy, as highlighted by Ahmad,
et al. [9]. Its substantial impact on the built environment and its capacity
to create ample employment opportunities for both skilled and unskilled
labors while fostering economic growth are widely recognized [10].
Furthermore, construction projects typically have a hybrid nature,
involving the use of both manufactured goods and essential resources
like raw materials and energy [11]. Like any other product, the quality
of a construction project is determined by the satisfaction of the client,
while the success of the project relies heavily on the performance of the
project team. Construction supply chain has been dened as: ‘a supply
chain in construction can be considered as a process of series of activities
transforming raw materials into nish products (e.g. roads or buildings)
and services (e.g. design or budget) for use by a client irrespective of
organization boundaries’ [12]. Furthermore, according to Kuei [13],
CSCM encompasses the management of information, ow, and nancial
aspects in the development of a construction project. Hatmoko and Scott
[14] provided a denition of CSCM as a collaborative system involving
suppliers, contractors, clients, and their agents. The objective is to
* Corresponding author at: Department of Civil Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3800, Australia.
E-mail address: robert.moehler@unimelb.edu.au (R.C. Moehler).
Contents lists available at ScienceDirect
Automation in Construction
journal homepage: www.elsevier.com/locate/autcon
https://doi.org/10.1016/j.autcon.2024.105396
Received 12 September 2023; Received in revised form 9 March 2024; Accepted 14 March 2024
Automation in Construction 162 (2024) 105396
2
coordinate the installation and utilization of information to deliver
various resources for construction projects, including materials, equip-
ment, labor, and temporary works. The CSCM concept inherently offers
the potential for signicant enhancements in client and stakeholder
value through a strategic focus on protability. Furthermore, the pri-
mary goal of managing a construction supply chain (CSC) is to plan and
direct the necessary quantities of materials to the site where nal as-
sembly takes place [15]. Furthermore, as can be seen from Fig. 1, con-
tractors, sub-contractors, designers, consultants, suppliers are just a few
of the numerous stakeholders that are engaged in the construction
sector. The contractor often engages numerous sub-contractors to fulll
various requirements in the construction project. These sub-contractors
may specialize in providing materials, machinery, skilled labor, un-
skilled labor, or any other specic needs that arise during the project.
Hence, there is a very broad variety of sizes within the contractor sector
as well, from tiny company owners to very huge international corpo-
rations. As a consequence, the supply chain in the construction sector
differs from that in manufacturing and is more complicated and un-
predictable [16]. Generally speaking, the implementation of SCM in
construction projects is challenging because of specic project charac-
teristics such as temporary involvement of multiple organizations [17],
short-term adversarial relationships [18], and difculties in managing
networks involving multiple stakeholders, materials and components
supply, and various services [19].
1.1. Articial intelligence (AI) in supply chain
Articial Intelligence (AI), as one of the paramount innovations in
digital technology, has monumentally transformed the landscape of
business operations, service processes, and productivity across a multi-
tude of industries [21,22]. It encapsulates a diverse range of tasks, akin
to learning, comprehension, estimation, problem-solving, advising, and
decision-making, which can be applied across an extensive spectrum of
disciplines, prominently in the eld of engineering design [23].
Furthermore, Sharma, et al. [24] have advocated for AI as an inuential
catalyst for value enhancement within supply chain processes. The
dynamism of AI techniques becomes evident as they underpin compet-
itive advantages, expediting automation, and digitalization with a
prowess surpassing traditional methodologies [25]. The diversity of AI
spans numerous sub-disciplines, including Machine Learning (ML) [26],
computer vision [27], robotics [28], Natural Language Processing (NLP)
[29], classication algorithms [30], fuzzy logic [31], optimization
[32,33], and automated scheduling and planning. Such facets of AI are
meticulously integrated to address a variety of business challenges and
facilitate effective decision-making in the face of real-world problems.
Fig. 2 shows visual representation that highlights the relationship be-
tween different branches of AI. While not all approaches in ML rely on
deep learning, it is a notable method for representation learning. The
main differences between various AI disciplines lie in how much they
rely on human intervention to dene rules and generate features for
solving problems. The eld of ML in AI focuses on developing algorithms
that can recognize patterns and make predictions using given datasets.
This process, called training, involves rening the algorithms to improve
their performance [34]. Additionally, there are representation learning
algorithms designed specically to acquire abstract features that effec-
tively describe data [35]. Deep learning is a subtype of representation
learning that constructs complex concepts by arranging simpler ones
hierarchically [36]. This intricate structure allows deep learning models
to capture intricate patterns and representations, making them well-
suited for various AI applications. However, ML approaches can be
broadly categorized into four main types: supervised, semi-supervised,
unsupervised, and reinforcement learning. These methods are widely
utilized in various domains today.
•In supervised learning, a human expert provides labeled examples to
a machine learning algorithm. The algorithm uses these labeled
Fig. 1. Construction Supply Network, inspired by [20].
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
3
examples to identify patterns and make predictions on new unla-
beled data. For instance, the algorithm can be trained on labeled
images of cats and dogs, allowing it to classify new unlabeled images
as either cats or dogs [37,38].
•Unsupervised learning algorithms can uncover patterns in data
without the need for pre-labeled examples. One common use for
these algorithms is clustering, which involves grouping together data
points that share similar characteristics. This process helps to reveal
hidden structures within the data [39].
•In reinforcement learning, behavior is shaped using rewards and
punishments. The algorithm takes actions within an environment
and receives feedback in the form of rewards or punishments. Over
time, it learns to maximize rewards and minimize punishments, ul-
timately optimizing its behaviors. Punishments can be seen as
negative signals that discourage specic actions [40].
An untapped domain of potential AI applications resides within the
burgeoning management ethos of SCM in the construction industry. This
philosophy necessitates an understanding of intricate, interconnected
decision-making processes and the development of intelligent knowl-
edge bases instrumental for collective problem-solving. Take, for
instance, the case of Eastman which restructured the thought processes
of veteran order pickers, thereby establishing a rule-based expert system
to optimize the path for order-picking within a warehouse [41]. This
approach could be adapted to construction supply chain, for selecting
the optimal path for material retrieval in a construction warehouse, thus
enhancing efciency and reducing wastage. Furthermore, to harmonize
a sequence of connected yet disparate stages of joint demand planning
and forecasting processes within the supply chain, Min and Yu [42]
advocated an agent-based forecasting system. This system, with the
ability to anticipate end customer demand via information exchange
among multiple supply chain partners and learn from previous fore-
casting experiences, could be critical for predicting the demand for
construction materials and thus, ensuring a smooth ow of resources. All
in all, AI has numerous advantages in SCM. It can optimize processes,
improve forecasting accuracy, enhance quality control, reduce costs,
and enhance safety conditions [24]. Efcient and transparent supply
chain operations are essential for success, and AI technologies have
played a vital role in meeting and surpassing these requirements [43].
1.2. Risk in construction supply chain
Risk is intrinsic to human activities and can result in catastrophic
events such as nancial crises, natural disasters, product failures, com-
modity price uctuations, and regulatory shifts. Risk is the variability
and uncertainty associated with the probabilities and subjective values
of potential outcomes [44]. Suppliers, clients, and internal production
processes all pose risks to the supply chain. These risks reduce the
overall effectiveness of the supply chain. Effective risk management
necessitates the identication of safety, quality, shortages, compliance,
legal issues, disasters, security threats, and terrorism-related risks.
Correctly identifying these risks enables effective risk management
[45,46]. The increasing complexity that results from globalization,
along with the streamlined structures and processes, are the primary
factors that drive the risks of the supply chain, ultimately making it more
vulnerable [47]. Moreover, the involvement of various stakeholders in
managing supply chains in construction industry, including suppliers,
manufacturers, retailers, logistics service providers, port authorities,
and government institutions at both national and international levels,
adds to the complexity and susceptibility of the supply chains [48].
Hence, supply chain risk management (SCRM) plays a key role in con-
struction project’s success. Supply chain risks can be categorized into
two types: (i) internal risks, also known as operational risks, and (ii)
external risks, also known as disruption risks [49]. Internal risks are
factors within the organization’s control that can negatively impact the
supply chain, such as capacity problems, information issues, end-user
problems, and other operational inefciencies. On the other hand,
external risks are factors outside of the organization’s control that can
disrupt the supply chain. These risks include economic issues, compe-
tition, political uncertainty, terrorism, and natural disasters. In addition,
the man-made risks that affect global supply chains are characterized by
uncertainty in terms of their type, location, and the supply chain part-
ners they impact, making them inherently complex and difcult to
manage.
While research exists exploring supply chain risk management across
industries [50], a systematic examination focused specically on the
construction industry remains needed. This literature review uniquely
investigates construction supply chain risk management practices across
an extensive time period of 1999–2023. Conducting a systematic review
spanning 25 years of scholarship will provide novel insights into the
evolution of knowledge and current state of research in this domain. The
construction sector faces distinct challenges and risks necessitating
Fig. 2. Articial Intelligence Branches.
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
4
focused study. By synthesizing ndings from construction-specic sup-
ply chain risk research over decades, this review will uncover key de-
velopments, trends, techniques, and gaps to be addressed. The main
goals are assessing the maturity of literature, revealing research clusters
and inuence, and informing future directions. This timely systematic
literature review centered on construction will elucidate risk sources,
strategies, methods, and technologies while clarifying drivers, extent,
and structure of scholarship in this emerging domain. The comprehen-
sive analysis from 1999 to 2023 will deliver an invaluable knowledge
base to enhance construction supply chain risk research and practice.
2. Materials and methods
In this research work, a systematic literature review (SLR), theme
analysis, and bibliometric analysis have been conducted on 4 April 2023
using Scopus and IEEE Xplore databases. The SLR methodology helps
identify key contributions, synthesize ndings, and offer reliability un-
available in single studies [51]. Scopus, which is considered the most
extensive abstract and citation database accessible to scholars, govern-
ment institutions, and business organizations [52], contains a vast
collection of over 1.8 billion cited sources dating back to the 1970s. IEEE
Xplore is a digital library that plays a vital role in the enginee-ring and
computing elds. It grants users web access to more than 4 million full-
text documents published by the prestigious Institute of Electrical and
Electronics Engineers (IEEE) as well as its publishing partners. Within
this vast database, researchers, students, and professionals in engi-
neering and computing can nd leading publications encompassing
electrical engineering, computer science, and electronics. This includes
highly cited journals and conference proceedings that are recognized
within these domains. By providing access to such an extensive collec-
tion of technical literature, IEEE Xplore serves as an invaluable resource
for individuals seeking high-quality content that undergoes a rigorous
peer-review process [53].
In conducting the SLR, the researchers utilized the PRISMA
(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
protocol. The PRISMA protocol is a well-established and widely recog-
nized framework that provides guidelines and standards for transparent
and comprehensive reporting of systematic reviews [54,55]. By
adhering to the PRISMA protocol, the researchers ensured that the SLR
process followed a rigorous and systematic approach, enhancing the
credibility and reliability of the review ndings [54]. The main goals of
this study are to: (i) map out the existing body of knowledge on supply
chain risk management in the construction industry by classifying cur-
rent research, (ii) pinpoint the most relevant articles on supply chain risk
management and risk taxonomy in construction, (iii) summarize the
common methods, tools, and AI-based techniques used in this eld, and
(v) highlight opportunities for future research.
To accomplish these goals, the research questions in Table 1 will be
addressed. Since RQ1 covers a broad topic, it has been divided into three
more specic sub-questions (RQ1.1 - RQ1.3) to provide a detailed
breakdown. Examining RQ1.1 - RQ1.3 will collectively yield a
comprehensive answer to overarching RQ1. Additionally, the other
research questions aim to synthesize key insights on methods, tools, and
AI applications for supply chain risk management in construction, as
well as future research needs. By thoroughly answering each research
question, this study seeks to fulll the stated objectives. This study
considered two different keyword strings for searching as follows:
•For research questions 1 and 2:
(“Supply chain” OR “Supply Chain Management” OR “Procurement”)
AND (“Construction” OR “Construction Industry” OR “Project Manage-
ment” OR “Infrastructure Project” OR “Building” OR “Mega Project”) AND
(“Risk” OR “Risk Management” OR “Risk Analysis” OR “Risk Assessment”
OR “Risk Identication” OR “Risk Prediction” OR “Uncertainty “)
•For research question 3:
(“Supply chain” OR “Supply Chain Management” OR “Procurement”)
AND (“Construction” OR “Construction Industry” OR “Project Manage-
ment” OR “Infrastructure Project” OR “Building” OR “Mega Project”) AND
(“Risk” OR “Risk Management” OR “Risk Analysis” OR “Risk Assessment”
OR “Risk Identication” OR “Risk Prediction” OR “Uncertainty”) AND
(“Machine Learning” OR “Articial Intelligence" OR “AI” OR “Deep
Learning” OR “Decision Making” OR “Reinforcement Learning” OR
“Neural Network”)
2.1. Inclusion and exclusion criteria
The search was not restricted to a specic time because the goal was
to gather as much relevant literature as possible up until the present
time. The search parameters were specically crafted to identify articles
that centered around the management of risk in construction supply
chains and the utilization of machine learning techniques in this
domain. To address the research questions, a set of criteria for inclusion
and exclusion were implemented. Table 2 shows the inclusion and
exclusion criteria in this research.
2.2. The screening procedures
The thorough screening procedure consisted of several stages: (i)
eliminating duplicated articles, (ii) excluding articles that did not meet
the inclusion criteria through an assessment of their titles and abstracts,
(iii) reviewing the full texts once more and eliminating articles that did
not meet the inclusion criteria, and (iv) extracting data from the
remaining articles after the ltration process (refer to Fig. 3). All articles
were imported into the Endnote software for the screening process.
Table 1
Research Questions.
RQ.1 What is the current state of construction supply chain risk management
(CSCRM) from 1999 to 2023?
RQ.1.1 What has been the trend in research publications on CSCRM from 1999 to
2023?
RQ.1.2 Who are the most active authors and inuential journals in the eld of
CSCRM research, and what are their signicant contributions?
RQ.1.3 What global dispersion of CSCRM research activity exists based on studies
by country?
RQ.2 How has the conceptualization of risk management phases and risk
classication evolved in CSCRM research between 1999 and 2023?
RQ.3 What traditional and common tools and techniques have been adopted for
managing construction supply chain risks from 1999 to 2023?
RQ.4 What are the impacts of AI in the management of risks within the
construction supply chain?
Table 2
Inclusion and Exclusion criteria in review procedure.
Inclusion Exclusion
(i
1
) Research must be conducted in
construction supply chain risk
management.
(e
1
) Research from conference
proceedings, book chapters, magazines,
news, and posters are excluded.
(i
2
) Research must be conducted in the
application of AI methods in
construction supply chain risk
management.
(e
2
) Research not related to risk
management in construction supply
chain.
(i
3
) Research must be published in peer-
reviewed journals.
(e
3
) Research conducted in other
languages.
(i
4
) Research must be reported in English.
(i
5
) Full text is available.
(i
6
) Source type must be Journal.
(i
7
) Publication stage must be Final.
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
5
3. Synthesis of results
Advanced statistical and graphical categorized tests were employed
using bibliometrics to analyze and summarize the data from the articles,
focusing on their spatio-temporal aspects. This approach ensures more
reliable and systematic results regarding the selected topic, minimizing
the risk of overlooking prior research contributions [56]. In the current
study, the VOSviewer (Visualization of Similarities) software was uti-
lized. The software facilitated the calculation and placement of topics on
a two-dimensional map, allowing for an accurate depiction of their
similarity or relatedness. The VOS mapping approach was employed for
this purpose. Additionally, the VOS clustering algorithm was used to
group the topics, with each category being represented by a unique color
[57,58].
Regarding Table 3, the keyword occurrence analysis reveals “risk
management” as the most prominent term in the literature, appearing in
22 documents with a total link strength of 38. This indicates the central
focus on risk management within construction supply chain research.
Other top keywords are “procurement” (9 occurrences, 23 link
strength), “construction” (8 occurrences, 18 link strength), “risk” (8
occurrences, 16 link strength), and “supply chain” (8 occurrences, 10
link strength). The prevalence of these risk and procurement related
terms demonstrates the emphasis on examining risks and procurement
processes in construction supply chain management. Notable keywords
like “construction supply chain”, “construction industry”, “supply chain
management”, and “project management” also feature highly, revealing
the key topics covered. More recent concepts like “COVID-19”, “machine
learning”, “blockchain”, and “building information modeling” are
emerging in the literature, reecting their growing importance. In terms
of link strength, “risk management”, “procurement”, and “construction”
have the strongest connections with other keywords, suggesting they are
frequently studied in conjunction with other topics. The total link
strength indicates the degree of interrelatedness between keywords in
Fig. 3. PRISMA search owchart of the current research.
Table 3
Keyword occurrence in literature review.
Keyword Occurrences Total link strength
risk management 22 38
procurement 9 23
construction 8 18
risk 8 16
supply chain 8 10
construction industry 7 10
construction supply chain 6 10
supply chain management 6 10
covid-19 5 8
supply chain resilience 5 9
project management 4 8
machine learning 3 5
blockchain 2 4
building information modeling (bim) 1 2
deep learning 1 1
articial intelligence 1 1
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
6
the literature. Analysis of keyword co-occurrences can provide further
insights into the relationships between key concepts. Overall, this
keyword occurrence analysis highlights risk management, procurement,
and construction as dominant themes. The growing connections of risk
with emerging technologies like ML and blockchain will be important to
monitor. As the eld progresses, it will be interesting to observe how
keyword frequencies and link strengths evolve over time. This analysis
provides a useful snapshot of the current keyword landscape in con-
struction supply chain risk management research. Fig. 4 shows the
VOSviewer keyword map.
RQ.1.1 How has the volume of CSCRM research publications
trended over time from 1999 to 2023?
An examination of publication volumes over time offers insights into
the evolving research landscape. This analysis highlights a signicant
uptick in papers focusing on construction supply chain risk management
in recent years. Regarding Fig. 5, from 1999 to 2015, the rate of pub-
lications relying on traditional non-AI methods remained low, averaging
only 1–2 papers annually. This trend suggests a relatively limited focus
on supply chain risks in construction during that time. However,
beginning in 2016, there has been a noticeable increase in papers uti-
lizing AI-based methods. This trend aligns with the growing prominence
of big data and the evolution of machine learning algorithms. Never-
theless, traditional methods continue to be prevalent. Over the last three
years (2021−2023), there has been a dramatic surge in research output.
As of 2023, there’s a near balance between research using traditional
methods (9 papers) and those using AI-based techniques (8 papers).
These patterns echo observations by Chen and Ying [59], who
emphasized the growing integration of AI and ML into construction
management research over the past decade. According to Ganesh and
Kalpana [60], AI has proven effective in risk assessment, prediction, and
decision-making within the construction sector. Conversely, Pan and
Zhang [61] pinpointed certain limitations of AI, especially when it
comes to capturing tacit knowledge and expert cognition, areas where
traditional methodologies excel. Such contextual intelligence remains
pivotal in construction risk management. However, while the adoption
of AI-based methods in construction supply chain risk research is in its
early stages, its pace is quickening. This shift creates avenues to harness
AI’s predictive capabilities alongside the depth of human contextual
understanding. Our review sheds light on publication trends and the
ongoing equilibrium between conventional and contemporary AI tech-
niques in this ever-evolving domain.
As can be seen from Fig. 6, in the last decade, there has been a sig-
nicant growth in research focused on construction supply chain risk
management. This is evident from the increasing number of citations in
this eld. In 2013, there were only 34 citations, but this number steadily
rose to 40 in 2014 and 38 in 2015. The interest and recognition in this
area peaked in 2016 with 56 citations, suggesting a growing interest in
the application of AI methods for risk assessment and decision-making
processes within construction supply chains. This upward trend
continued over the next few years, with a further increase to 74 citations
in 2017 and 69 citations in 2018. By the year 2019, there was a notable
milestone as citations surpassed triple digits with a total of 102. This
indicated that there is now a maturing body of knowledge on con-
struction supply chain risk management. Despite the challenges posed
by the global pandemic, research efforts in this area remained strong. In
fact, even amidst unprecedented circumstances, research output
increased signicantly to153 citations in 2021. The years 2022 and 2023
saw continued growth with citation numbers doubling from the previous
year to 264 and 263 respectively. This exponential rise over the past
decade highlights how construction supply chain risk management has
become increasingly relevant and signicant within both academia and
industry circles. The sustained dedication to advancing knowledge and
practices reinforces its importance within this critical domain.
RQ.1.2 Who are the most active authors and inuential journals in
the eld of CSCRM research, and what are their signicant
contributions?
In terms of total documents published, the most productive authors
are Koc K., Liu J., Shen G.Q., and Chan A.P.C., each with 3 documents.
This indicates their prominent contribution to the literature in this
domain. The authors with the most citations are Liu Y. (130 citations),
Wang Y. (131 citations), and Zou P.X.W. (133 citations), highlighting
the impact and inuence of their work. Analyzing total link strength,
which represents connectedness to other authors, Chan A.P.C. has the
highest value (13), followed by Liu Y. (8), Li X. (8), and Mohammed A.
(11). This suggests these authors are frequently co-authoring papers and
have stronger connections in the collaborative author network.
Fig. 4. Keyword occurrence schematic presentation.
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
7
Other highly linked authors, based on co-authorships, include
Gosling J., Naim M., Jabarzadeh Y., Samson D., and Wang D., although
they have fewer total publications. In terms of individual contribution,
the authors with the most independent citations are Gosling J. (58),
Naim M. (58), Liu Y. (130), and Zou P.X.W. (133). This indicates the
signicance of their independent work, distinct from co-authored pub-
lications. The emerging authors to watch, based on their strong
connectedness despite fewer documents, are Chan A.P.C., Mohammed
A., Li X., and Liu Y. They are actively collaborative and potentially rising
in inuence. This author analysis provides insights into both produc-
tivity (documents published), impact (citations), and connectedness (co-
author link strength) of the prominent scholars in this research eld.
Tracking these author metrics over time can reveal the evolving
contributor landscape (See Fig. 7).
Based on Fig. 8, the journal with the most published documents on
construction supply chain risk management is “Sustainability
(Switzerland)” with 7 documents. This indicates its leading role in
disseminating research in this domain. In terms of impact, the journal
with the most citations is “Construction Management and Economics”
with 324 citations, followed by “Journal of Management in Engineering”
(166 citations) and “International Journal of Production Economics” (22
citations). This highlights the inuence of publications in these outlets.
The journals with the strongest connections to others, based on co-
citations, are “Engineering, Construction and Architectural
Fig. 5. Number of publications from 1999 to 2023 (as of 4 April 2023).
Fig. 6. Number of citations from 2013 to 2023 4 April 2023).
Fig. 7. Authors co-occurrence diagram.
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
8
Management” (total link strength 35), “Journal of Construction Engi-
neering and Management” (77), and “International Journal of Con-
struction Management” (27). This suggests these journals frequently
publish related research that gets co-cited. Other well-connected jour-
nals include “Construction Innovation”, “Industrial Management and
Data Systems”, and “Buildings”, implying a degree of overlap or com-
plementary research across these outlets. Emerging journals to watch
include “Sustainability (Switzerland)” and “International Journal of
Production Economics” which have high numbers of documents but
fewer citations currently, indicating potential growth. Less integrated
journals, with few co-citations, are “Computers in Industry”, “Canadian
Journal of Civil Engineering”, and “Transportation Research Part E",
revealing opportunities for increased cross-pollination. Tracking journal
metrics over time can provide insights into how research networks and
boundaries are evolving in construction supply chain risk management
literature.
Table 4 provides a structured synthesis of key research themes, their
principal contributions, and the subsequent impacts on construction
supply chain risk management. It also highlights potential gaps to guide
future studies, all sourced from various esteemed academic journals.
This comprehensive overview allows for quick insights into the state of
research in construction supply chain risk management across a range of
journals and topics.
RQ.1.3 What global dispersion of CSCRM research activity exists
based on country of study?
Regarding Figs. 9 and 10, China leads the global arena with 29
publications, indicative of its robust scientic output in the realm of
construction supply chain risk management. The UK and Australia
follow suit with 17 and 12 documents, respectively, while the USA has
also made a signicant mark with 10 contributions. It’s noteworthy that
regions like Hong Kong and Pakistan have displayed considerable in-
terest, each contributing six publications. However, numerous countries
such as Malaysia, the Netherlands, Brazil, Italy, among others, have
recorded a solitary publication during the studied timeframe.
The surge in China’s contributions can likely be attributed to its
burgeoning construction sector coupled with the pace of its urbaniza-
tion. The UK’s substantial footprint in this area possibly stems from its
sophisticated construction industry and academic rigor. Australia’s
commendable participation might hint at its focus on meticulous supply
chain risk management, particularly when confronted with challenges
like climate-induced risks [62]. Interestingly, while publication count
provides an initial glimpse, it doesn’t always portray the full picture of
inuence. For example, the UK, despite having fewer publications than
China, outshines in citations with a count of 395, as opposed to China’s
234 and Australia’s 209. This disparity accentuates the profound impact
and inuence of the UK’s research in this domain. Additionally, Hong
Kong’s research, with its 6 publications, has amassed 176 citations,
indicating its relevance and signicance.
Assessing the collaborative dimension of the research, the total link
strength becomes instrumental. The UK again takes the lead with a
strength of 28, whereas the USA and China showcase strengths of 17 and
15, respectively. It’s intriguing that China and Australia exhibit analo-
gous collaboration levels, even with China’s greater number of
publications.
Several trends and observations surface:
•Pakistan, with its 6 publications and a link strength of 12, indicates a
burgeoning inuence or commendable collaborative initiatives.
•Notable citation anomalies include Norway, which, despite two
contributions, has no citations, suggesting its research is either
nascent or yet to gain broad recognition. Conversely, Sweden’s pair
of publications have garnered a remarkable 100 citations, empha-
sizing their profound inuence.
•Bhutan’s single publication, having accumulated 64 citations, sig-
nies its signicant research impact.
Further delving into the collaborative aspect, the existence of seven
distinct clusters suggests the presence of diverse research groups or af-
liations in this domain. These clusters might be inuenced by regional
associations, shared methodologies, or even common challenges. High
interconnectivity is evident with 60 links across these countries, signi-
fying co-authorships, mutual citations, or thematic alignments. This
level of interconnectedness heralds the seamless knowledge transfer
across frontiers, fostering mutual growth. The overall link strength of 79
Fig. 8. Number of publications per each journal from 1999 to 2023.
M. Baghalzadeh Shishehgarkhaneh et al.
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9
Table 4
The top contributions from the rst 10 journals in the eld of CSCRM.
Journal Research Themes & Trends Key Contributions Journal’s Impact on Construction
Supply Chain Risk Management
Potential Gaps
Sustainability Blockchain in construction
supply chain
Enhanced transparency Advocates for technology-
driven transparency and
resilient supply chains
Need for empirical validation of Blockchain’s actual
efcacy in real-world construction projects.
Prefab building info-sharing Operational efciency Exploration needed on barriers to information-
sharing across different construction project
stakeholders.
Machine learning in risk
assessment
Advanced risk assessments in
Industry 4.0
Require in-depth case studies on machine learning’s
false positives and potential oversights in assessing
construction risks.
Semiconductor supply
resilience
Collaboration between
stakeholders
Research gap in mapping the entire semiconductor
supply chain, highlighting critical choke points.
AI-centric methodologies in
supply chain risk
Merging expertise with AI
insights
Studies required on AI’s limitations and potential
biases when assessing and mitigating construction
supply chain risks.
Engineering
Construction and
Architectural
Management
Modular Construction
complexities
Addressing modular
uncertainties
Spotlights modular construction
dynamics and offers strategies
for uncertainties
Comparisons needed between modular and
traditional construction regarding performance,
speed, and quality.
Lean production in
prefabrication
Production optimization Further research on integrating lean principles with
emerging technologies like IoT in prefabrication.
Green projects and
associated risks
Risk strategies for sustainable
projects
Need a unied framework to assess and mitigate
ecological and nancial risks in green projects.
COVID-19 impacts on supply
chain risk
Emphasizing resilience
during disruptions
Comparative analyses needed on pre-COVID vs
post-COVID risk management strategies’ efcacy.
International Journal
of Production
Economics
Subcontracting & rework
costs
EPC contracts implications Delves into intricate nancial
and contract-related risks,
enhancing project outcome
predictability
Absence of quantitative models addressing regional
disparities in subcontracting and cost-sharing
practices.
Inuence of supply chain
risks on nancials
Relationship between risks &
nancial impact
Limited utilization of AI-driven predictive analytics
to measure individual risk impacts on nancial
metrics.
Probabilistic decision-
making model
Economic sustainability &
knowledge management
Lack of integration between Bayesian networks and
the sustainable supplier selection (SSS) framework
to enhance probabilistic decision outcomes.
Resilient supplier selection Supplier complexity
considerations
Underutilization of machine learning techniques in
evaluating supplier resilience metrics within
dynamic market scenarios.
Collaborative mega project
supply chains
Infectious risk propagation Sparse application of graph theory methods to
model and mitigate cascading disruptions in
interconnected mega projects.
Journal of
Construction
Engineering and
Management
Health & Safety
Coordination
Challenges in health & safety
risk management
Proposes robust health and
safety coordination strategies
and risk mitigation
Integration real-time data analytics to proactively
address health & safety risks during the design
phase, considering varying global regulations and
best practices.
Fuzzy Decision-Making &
Green Suppliers
Advanced decision-making
meets environmental risks
Balancing between optimal decision-making and
ensuring environmental sustainability, especially in
the context of volatile green material markets and
their impact on construction supply chain stability.
Late Deliverables Addressing and
understanding delays
Implications of emerging technologies, like the
Internet of Things (IoT) and AI, in predicting,
preventing, and managing late deliverables in the
construction supply chain.
Counterfeiting in Supply
Chain
Vulnerabilities and
counterfeit mitigation
Leveraging blockchain and other traceability
technologies to ensure material authenticity
ETO Supply Chain risk Identifying and categorizing
risks
Development of adaptive models that can
accommodate dynamic changes specic to ETO
projects
Buildings Prefabricated Building Risk
& EPC Contracting
Construction & design risk
insights
Emphasizes risk management in
prefabricated building contracts
and design processes
Integration of SEM with Monte Carlo simulations in
assessing uncertainty levels
PPP Infrastructure
Operational Risk
Management
Systematic operational risk
strategies
Lack of dynamic system modeling approaches, such
as System Dynamics, to explore feedback loops and
their impact on operational risks within PPP
infrastructure projects.
Supply Chain Risks &
Resilient Capabilities in
Construction
Vulnerable risk identication Inadequate studies employing Neural Networks to
forecast and adapt to evolving risk patterns and
enhance resilience
Risk Factors & Transmission
in International EPC Project
Procurement
Domino effect of
procurement risks
Sparse application of Agent-Based Modeling in
simulating risk transmission across EPC
procurement
Construction
Innovation
Value for Money (VFM) &
Smart City PPP Projects
VFM factors in smart
infrastructure
Innovates in value assessment
and strategic partnerships for
sustainable infrastructures
Using Data Envelopment Analysis (DEA) to
benchmark
Procurement Innovation &
Sustainable Building
Renovation
Danish strategic partnerships
model insight
Absence of Comparative Case Study methods to
juxtapose the Danish model against other European
models for sustainable building renovation
procurement and to identify best practices.
(continued on next page)
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
10
further accentuates the intensity of these collaborative endeavours,
hinting at deep-rooted academic partnerships or unied objectives in
addressing supply chain challenges.
RQ.2 How has the conceptualization of risk management phases
and risk classication evolved in CSCRM research between 1999 and
2023?
Table 4 (continued )
Journal Research Themes & Trends Key Contributions Journal’s Impact on Construction
Supply Chain Risk Management
Potential Gaps
Vulnerabilities &
Capabilities in Malaysian
Construction Supply Chain
Insights on political/
regulatory and market
pressures
Using Bayesian Network modeling to map and
predict the cascading effects of political/regulatory
shifts
Industrial
Management and
Data Systems
Deep Learning & Time-series
Interpolation for Credit Risk
TD-LSTM for monitoring
irregular time-series
Advances in deep learning
applications for better risk
assessment in construction
Exploration of incorporating Attention Mechanisms
or Transformer models in enhancing the capabilities
of TD-LSTM for capturing long-term dependencies
in irregular construction nancial time-series data.
Text Mining & Factors in
Supply Chain Financing Risk
Management
Key risk management factors
identied
Leveraging Topic Modeling, such as Latent Dirichlet
Allocation (LDA), to discover latent topics or
emergent risk factors from vast repositories of
unstructured construction nancing documents.
Supply Chain Risks & Delays
in Construction Project
Dynamic modeling benets
for risk assessment
Employing Agent-Based Modeling (ABM) to
simulate and understand emergent behaviors,
interactions, and ripple effects of supply chain
disruptions in large-scale construction projects.
Journal of
Management in
Engineering
Shapley Additive
Explanations & Resampling
Algorithms for PPP Projects
ML model for predicting PPP
project failure
Uses advanced ML techniques to
predict and interpret supply
chain risks in PPP projects
Utilizing Gradient Boosting Machines (GBM)
coupled with SHAP in addressing class imbalance
for predicting rare events (like project failures) in
PPP construction projects
Life Cycle Risks in
Construction Supply Chain
Risk categorization with
respect to life cycle
Employing Natural Language Processing (NLP) tools
to mine and quantify sentiments or concerns from
stakeholders’ discussions, providing a more
nuanced understanding of life cycle risks and their
impacts on construction supply chains.
Supply Chain Risks in
Prefabricated Building
Projects in Hong Kong
Risk exploration using social
network analysis
Leveraging Graph Neural Networks (GNN) to
analyze and predict risk propagation in supply
chains based on the network structure of
stakeholders in the construction industry
Automation in
Construction
Modeling Supplier Selection
in Fuzzy Scenario-Based
Environment
Novel multi-objective mixed
integer linear programming
model introduction
Focus on optimizing supplier
selection processes using
advanced models
Exploration of integrating Machine Learning (ML)
models, such as Random Forest or Gradient Boosted
Trees, with fuzzy logic to rene scenario
predictions, potentially increasing the accuracy of
estimates and enhancing the performance of the
supply chain decision-making model.
Decision Support for
Offshore Asset Construction
with Supply Disruptions Risk
Emphasizing the signicance
of considering disruption risk
Incorporating Bayesian Networks (BNs) for
probabilistic modeling and uncertainty
quantication in the context of offshore asset
construction
Canadian Journal of
Civil Engineering
Critical Supply Chain Risks
in IEPC Projects
Interconnections in IEPC;
Social network analysis
Deepened risk identication
and understanding
Limited risk mitigation strategy evaluation.
Risk ID & Assessment Micro risk breakdown; Risk
responsibility matrix
Scalability of fuzzy model in larger projects
Fig. 9. The global distribution of research papers related to CSCRM.
M. Baghalzadeh Shishehgarkhaneh et al.
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Fig. 11 provides a historical overview of the risk management phases
in CSCRM from 1999 to 2023. The initial phase focused on risk identi-
cation, which involved pinpointing potential risks to the construction
supply chain, documenting their characteristics [63], and then evalu-
ating and ranking these risks in terms of impact and likelihood during
the assessment phase [64]. These stages formed the basis of risk analysis
[65], setting the stage for subsequent developments in the eld. As
CSCRM matured, additional phases like risk controlling and risk allo-
cation were integrated, with risk allocation specically concerning the
systematic distribution of identied risks among project stakeholders, as
per Hwang, et al. [66] discussed, a process deeply reliant on clear
communication and collaborative negotiation of contractual clauses
[67]. In other words, effective risk allocation occurs through the
collaborative negotiation of contractual clauses, intended to document
the agreements reached among multiple parties involved in a con-
struction project. Successful negotiation relies on clear and efcient
communication regarding potential risks, a well-known challenge
within the construction industry [68]. Advancements continued with
the integration of risk evaluation, prioritization, and treatment, broad-
ening the scope and depth of the risk management process. In 2022,
Project Management Institute (PMI) framework introduced a series of
stages that included risk planning, qualitative and quantitative analysis,
response planning, and monitoring [69]. Concurrently, the ISO 31000
and EN IEC 31010 standards provided a structured approach,
emphasizing planning, managing information, applying risk techniques,
and documenting the risk process [70]. These guidelines established a
foundation for standardized practices in CSCRM.
In the CSCRM eld, there has been a distinct shift over time from a
primarily reactive stance towards a more proactive and strategic risk
management approach. Initially centered on the identication and
response to immediate risks, the discipline has evolved to include a
thorough evaluation, prioritization, and treatment of risks, reecting a
more anticipatory and holistic view of risk management. The addition of
the risk recovery phase in 2023 marks a critical juncture in this evolu-
tion, emphasizing the importance of resilience and the need for rapid
restoration of normal operations post-risk event. This phase is particu-
larly focused on minimizing the long-term adverse impacts and facili-
tating a swift return to business as usual.
This development underscores a broader transition in CSCRM from
traditional reactive measures to a forward-looking, strategic risk man-
agement framework. The eld has grown to appreciate the value of not
just managing but recovering from risks, signifying a mature approach
that prioritizes operational continuity and resilience. The integration of
global standards and frameworks such as those from PMI, ISO, and CEN,
has further anchored CSCRM practices in a cycle that is both dynamic
and reective of international benchmarks. Consequently, this has pre-
pared the industry to adeptly manage the complexities of modern con-
struction supply chains, setting new standards for excellence and
Fig. 10. Bibliographic coupling for countries.
Fig. 11. The evolution of risk management phases in CSCM from the past to the present.
M. Baghalzadeh Shishehgarkhaneh et al.
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12
adaptability in risk management. The industry’s commitment to
continually enhancing risk management practices ensures resilience and
success in the face of uncertainties, thereby advancing the discipline’s
capability to thrive in a landscape marked by constant change and
emerging risks.
However, as can be shown in Fig. 12, the inconsistent prioritization
of the risk identication phase throughout the period indicates the need
for more consistent attention to identify risks within the construction
supply chain. By establishing a systematic process for identifying risks
and promoting a risk-aware culture, stakeholders can ensure that po-
tential risks are recognized and addressed at an early stage, minimizing
their impact on projects [71]. The relatively consistent engagement in
the risk assessment phase signies a growing emphasis on assessing risks
within the construction supply chain. It is essential to continue this trend
by adopting robust assessment methodologies, such as qualitative and
quantitative risk assessment techniques, to thoroughly evaluate the
potential impact and likelihood of identied risks. This will enable
stakeholders to make informed decisions and allocate appropriate re-
sources to manage these risks effectively [72,73]. The limited iterations
in the risk analysis phase highlight a potential gap in conducting thor-
ough analyses of identied risks. To bridge this gap, it is crucial to
dedicate resources and expertise to conduct comprehensive risk ana-
lyses, including considering the root causes, potential consequences, and
interdependencies of identied risks. This will provide a deeper under-
standing of the risks and enable the development of targeted mitigation
strategies.
However, the sporadic involvement in the risk allocation phase
suggests a lack of consistent attention to assigning responsibilities and
resources for managing identied risks. By establishing clear roles and
responsibilities, dening accountability, and allocating adequate re-
sources, stakeholders can ensure effective risk allocation. This will
enhance coordination among project participants, promote proactive
risk management, and facilitate timely and efcient risk response ac-
tions. The intermittent engagement in the Risk Mitigation phase em-
phasizes the need for sustained efforts in implementing strategies to
reduce or eliminate risks within the construction supply chain. By
combining risk mitigation activities with other phases, such as risk
assessment and risk allocation, stakeholders can adopt a holistic
approach to risk management. This integrated approach will enable the
proactive implementation of mitigation measures, including risk pre-
vention, risk transfer, and contingency planning, to minimize the like-
lihood and impact of identied risks [74]. There is a need for more
reliable techniques to prioritize risks according to their potential effect,
as shown by the limited iterations in the risk prioritization phase.
Stakeholders may successfully prioritize risks by setting explicit criteria
and considering elements like likelihood, severity, and project goals.
They will be able to concentrate their efforts and resources on control-
ling the most important risks as a result, guaranteeing optimal effort
allocation, and raising overall risk management effectiveness. So, risk
management in the supply chain for construction must be broad and
proactive. Stakeholders may improve their capacity to identify, assess,
analyze, allocate, mitigate, and prioritize risks effectively by addressing
the gaps found in risk analysis, risk allocation, and risk prioritization. In
the end, this will result in better accomplishments, lower costs, and more
stakeholder satisfaction, building a more resilient and prosperous con-
struction supply chain.
In the literature, construction supply chain risks have been catego-
rized differently by various researchers. Dada and Jagboro [75] classi-
ed risks into human and natural categories, where human risks stem
from within human-organized systems, while natural risks occur inde-
pendently of human inuence. Human risks in construction supply
chains include social, political, economic, nancial, legal, health,
managerial, technical, and cultural risks, while natural risks encompass
weather and geological factors. Perry and Hayes [76] identied central
risk sources specic to building works, including physical, environ-
mental, design, logistics, nancial, political, legal, time schedule slip-
page, construction, and operational risks. On the other hand, Gosling,
et al. [77],Christopher and Peck [78], grouped risks into process, supply,
demand, and control categories. Process risks arise from internal issues
like scrap and re-work, while supply risks result from unreliable sup-
pliers causing long lead times. Demand risks relate to customer-specic
challenges, and control risks involve managing orders and raw material
ow.
Ritchie and Brindley [79] divided supply chain risks into systematic
and unsystematic categories. Systematic risks arise from the internal
operating environment and external factors beyond an organization’s
immediate control, while unsystematic risks are specic to the organi-
zation and can be managed internally. Pettit, et al. [80] categorized
supply chain risks into seven primary groups: turbulence, threats, con-
nectivity, external pressures, resource limits, sensitivity, and supplier or
customer disruptions. These categories include a wide range of risk
factors such as natural disasters, price uctuations, political instability,
theft, terrorism, and inter-relationships with external entities. Other
researchers have also provided additional classications of supply chain
risks. Financial risks, including price uctuations and pressures, can
signicantly impact construction organizations in developing countries,
leading to cost overruns, and affecting project scope and performance.
Operational risks encompass supplier disruptions and product avail-
ability challenges. Strategic risks are linked to managing complex supply
networks and unproductive planning. Hazards risks relate to natural
Fig. 12. Number of publications based on CSCRM phases.
M. Baghalzadeh Shishehgarkhaneh et al.
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13
disasters [81], while organizational risks are within the direct control of
the organization. Lastly, external risks lie beyond the direct control of
the company and its supply chain [82]. Understanding this diverse range
of risk classications is crucial for implementing comprehensive risk
management strategies to safeguard the efciency and resilience of
supply chain operations. Marandi Alamdari, et al. [83] categorized
supply chain risks into macro risks, including natural and man-made
risks, and micro risks, specically operational risks. Within micro
risks, they further subcategorized supply and demand risks, as well as
manufacturing and infrastructural risks.
As depicted in Fig. 13, this study undertook an exhaustive review of
construction supply chain risks (SCRs), and classied them into two
main categories: micro and macro risks, as indicated by Marandi
Alamdari, et al. [83]. The micro risks have been divided into nancial
risks, timeline deviations, operational, contractual, managerial, tech-
nical, design, and social. This classication is in line with ndings from
[84], which highlighted the importance of managing these micro risks to
ensure construction project success. Central to micro-level risks within
the construction supply chain is the challenge of nancial liquidity.
Regardless of the project’s magnitude, a consistent cash ow is crucial.
O’Brien, et al. [85] underlined that nancial shortfalls could derail even
the most meticulously planned supply chains. Scheduling within the
supply chain mirrors a ripple effect, and disruptions in one link can
cascade, affecting subsequent stages. In other words, like a chain, each
link in the construction supply chain depends on the strength of the
previous link. Any weak link that causes disruption will pull the entire
chain out of alignment. Proactive scheduling is crucial, but so is building
contingencies and exibility [86]. The nuances of the construction
supply chain, from vendor selection to on-time delivery, present myriad
risks. The complexity of procurement choices within the chain and the
consequential ripple effects. Furthermore, ambiguities in supply chain
contracts can become breeding grounds for disputes, resulting in further
lags [87].
Furthermore, In construction supply chain management, it’s impor-
tant to skillfully balance two things: making sure all parts t together
smoothly and promoting a culture where people are responsible and
open with each other [88]. Srivastava [89] asserted that managerial
risks impact not just operational efcacy but also erode the foundational
trust between supply chain partners. Technological innovations, though
promising, introduce a layer of complexity to the supply chain. While
Fig. 13. Risk categories and types in CSCM in the literate.
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
14
they hold the potential to rene supply chain processes, the challenges
of integration and skill acquisition are real [90]. The human element
within the construction supply chain carries its own set of risks. Effective
communication and relationship management among stakeholders are
instrumental, and any shortcomings therein can deteriorate both morale
and end outcomes [91].
On the other hand, the macro risks encompass a range of issues such
as physical risks, sustainability-related risks, logistic challenges, envi-
ronmental impacts, geological hazards, economic and nancial uctu-
ations, legal constraints, health and safety considerations, political
instabilities, and cultural aspects. This aligns with Bode, et al. [92] who
argued that understanding these macro risks is crucial for improving
supply chain resilience and adaptation capabilities. Macro-type risks,
such as sustainability, logistics, economic & nancial, and political risks,
have received more attention in the literature. Mok, et al. [93] argue
that addressing sustainability risks is critical for promoting environ-
mentally friendly practices and social responsibility. Managing logistics
risks is important to ensure the efcient movement of materials and
resources throughout the construction process [94]. With respect to
economic & nancial risks, Odeyinka, et al. [95] highlight the impor-
tance of these considerations to safeguard the project’s nancial health
during economic uctuations. Mitigating political risks is also crucial to
maintain project stability and avoid potential disruptions caused by
changes in government policies or geopolitical events [96]. Prioritizing
research and risk management efforts on these macro-type risks thus
empowers the construction industry to create a sustainable, resilient
foundation for its projects [76].
In an evaluation of the construction supply chain risks, the data in-
dicates a pronounced emphasis on micro-level risks, tallying 236 in-
stances, as opposed to the macro risks which are recorded at 118
instances. This distinction showcases the intricate challenges inherent in
the day-to-day operations of construction projects. When dissecting
micro risks further, both Operational and Managerial concerns emerge
as particularly signicant, with each registering 38 instances. Such g-
ures underline the critical importance of streamlined operations and
effective management in ensuring project success. Financial micro risks,
touching upon areas like delayed payments and cash ow, are also
substantial with 23 instances. On the broader, macro scale, nancial
risks reign supreme with 37 instances. This category encapsulates issues
such as ination rates and market saturation, which can substantially
inuence the overall nancial health and feasibility of construction
projects. Legal concerns, with a count of 22, highlight the myriad reg-
ulatory and compliance challenges construction projects often grapple
with. Changes in regulations, for instance, can introduce unanticipated
complications and costs. Conclusively, while the data underscores the
salience of micro-level concerns like Operational and Managerial chal-
lenges, it simultaneously accentuates the gravity of Financial and Legal
macro risks in the broader construction supply chain framework. The
balance between addressing immediate, project-specic issues and
overarching externalities is thus pivotal.
RQ.3 What traditional and common tools and techniques have been
adopted for managing construction supply chain risks from 1999 to
2023?
Delving into the landscape of methodologies employed in construc-
tion supply chain risk management research, it becomes evident that a
wide array of tools and techniques have been utilized, based on Table 5.
The empirical approach, primarily utilizing case studies (4.6% of
methods used), presents an opportunity for in-depth analysis of real-
world phenomena within a specic context. Case studies are benecial
for exploring complex issues in their natural settings, providing a rich
understanding of the eld, and can be particularly useful for theory
generation [97]. On the downside, the ndings from a single case study
may lack generalizability due to the specicity of the context, and their
value is often seen as more exploratory than conrmatory [98]. Survey
methods, including interviews and questionnaires, which represent a
signicant 33.59% of methods used, provide an efcient way of
Table 5
Overview of common methods in CSCRM.
No Categories Methods/tools Type Number Percentage
(%)
1 Empirical Case Study Qual 6 4.68
2 Survey Interview Qual 19 14.84
Questionnaire Mixed
Methods
24 18.75
3 Literature
Review
SLR Qual 18 14.06
4 Simulation Monte Carlo Quan 3 2.34
5 Holistic
Analysis
Frameworks
Systems Thinking Quan 4 3.12
Workshop Qual 1 0.78
6 Team-based Group discussion Qual 2 1.56
Brainstorming
sessions
Qual 2 1.56
Analytic
Hierarchy
Process (AHP)
Quan 3 2.34
Fuzzy Cognitive
Maps (FCMs)
Quan 2 1.56
7 Multi-criteria
Decision
Making
Analytic Network
Process (ANP)
Quan 1 0.78
Interpretive
Structural
Modeling (ISM)
Quan 3 2.34
Process Approach
(PA)
Qual 1 0.78
Qualitative
Comparative
Analysis (QCA)
Qual 1 0.78
8 Visits Site visits Qual 2 1.56
9 Grounded
theory
– Qual 4 3.12
10 Social
Network
Analysis
(SNA)
– Quan 2 1.56
11 Grey
Relational
Analysis
(GRA)
– Quan 2 1.56
12 Exploratory
Factor
Analysis (EFA)
– Quan 1 0.78
13 Structural
Equation
Modeling
(SEM)
– Quan 1 0.78
14 Statistical
Analysis
Cronbach’s Alpha Quan 7 5.46
The Shapiro-Wilk
test
Quan 5 3.9
The Mann-
Whitney U test
Quan 4 3.12
KMO test Quan 2 1.56
The Wilcoxon
Signed-Ranks test
Quan 2 1.56
Kendall’s
coefcient of
concordance
Quan 2 1.56
Bartlett’s test of
sphericity
Quan 1 0.78
Kruskal-Wallis
tests
Quan 1 0.78
Multivariate
Regression
Analysis
Quan 1 0.78
The Spearman’s
coefcient of
rank correlation
Quan 1 0.78
Qual: Qualitative; Quan: Quantitative.
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gathering data from a large sample size. These methods enable re-
searchers to understand the perceptions, attitudes, and experiences of
the sample group, and allow for statistical analysis [99]. Furthermore, in
the risk identication and assessment phases of the CSCRM, during
which prospective risks are identied and assessed, survey techniques
could be considered one of the benecial tools. These techniques can
acquire data from a vast array of stakeholders and parties, thereby
identifying a vast the number of potential risks and evaluating the po-
tential impact and likelihood of each risk based on their previous ex-
periences and prospects. Nonetheless, these methods can have biases
including response bias, nonresponse bias, and selection bias. The design
of survey questions may also impact the validity of the results [100].
Team-based methods such as workshops, group discussions, and brain-
storming sessions are valuable for generating multiple perspectives,
encouraging participant engagement, and facilitating the exchange of
ideas [101]. However, they are susceptible to groupthink, where pres-
sure for consensus might override the realistic evaluation of alternative
viewpoints. The dynamics within the group may also inuence the
outcomes [102].
Although less often employed, simulation techniques such as Monte
Carlo and ST provide the capacity to simulate large and dynamic sys-
tems, allowing for the study of alternative scenarios and outcomes. The
Monte Carlo simulation is a simulation methodology that utilizes iter-
ative random sampling and statistical analysis to calculate outcomes.
The present simulation methodology has a strong correlation with
random experiments when the anticipated outcome is not pre-
determined. In the present context, the use of Monte Carlo simulation
may be seen as a systematic approach to doing what-if analysis [103].
The probability distribution of identied risks can be evaluated using
the Monte Carlo simulation. As a result, stakeholders in the construction
supply chain can take more intelligent decisions about whether to
continue with or modify certain supply chain operations based on the
insightful information about the extent of the risk provided by this
strategy [104,105]. These approaches, however, need a large quantity of
data for model construction, and the quality of their outputs is often
dependent on the validity of the underlying assumptions [106]. On the
other hand, multi-criteria decision-making tools (AHP, ANP, ISM, PA,
and QCA) are ideal for dealing with complicated decision-making situ-
ations incorporating several criteria and options. The AHP (Analytic
Hierarchy Process) provides a simple and customizable way for
analyzing CSCRs. This multi-criteria decision analysis approach makes it
possible to include both subjective and objective factors in the evalua-
tion process, which is essential for successful risk assessment. Instead of
replacing intuition, this novel strategy tries to strengthen and enhance
it, giving management with a more solid platform for decision-making.
In the context of CSCRM, the AHP is particularly useful for contractors
assessing the risk quotient of a project they may be bidding on. It pro-
vides a systematic framework for risk analysis and assessment [107].
However, when making decisions, it is often necessary to consider in-
teractions and feedback across levels since risk assessment and decision
analysis are often too complicated to be organized hierarchically. The
AHP approach may not be precise for such tough decisions. Saaty [108]
proposed the analytical network process (ANP), a more comprehensive
strategy than AHP, as an alternative. ANP does not make the assump-
tions of internal and external independence and instead deals with non-
hierarchical decision problems [109]. Furthermore, ISM (Interpretive
Structural Modeling), in the context of CSCRM, is a qualitative approach
that, as mentioned by Malone [110] and Watson [111], offers solutions
to complex supply chain challenges by outlining the structural re-
lationships of various components. These elements t together into a
structure inside the ISM framework based on certain relationship types
that indicate their linkages. Fundamentally, ISM helps to recognize and
arrange the complex linkages that exist within the building supply chain,
providing a thorough comprehension of the interactions and affects
among various elements. These methods provide a systematic and
transparent assessment procedure that ensures all important factors are
taken into account [112]. Nonetheless, they need expert input and entail
some subjectivity, which may impact the outcomes [113].
Advanced methods such as Social Network Analysis (SNA), Grey
Relational Analysis (GRA), Exploratory Factor Analysis (EFA), and
Structural Equation Modeling (SEM) can uncover complex relationships,
patterns, and constructs within data, allowing for a more in-depth un-
derstanding of the phenomena under study. These strategies, however,
need a large amount of data as well as a high degree of statistical skill
[114]. Similarly, the statistical analysis tools such as Cronbach’s Alpha,
Shapiro-Wilk test, Mann-Whitney U test, and others, contribute to the
rigor and reliability of the research ndings by allowing for hypothesis
testing, correlation analysis, and data distribution assessment [115].
Nonetheless, these techniques require a meticulous application,
considering the nature of the data and the research questions, to avoid
misleading conclusions [116]. The study shows how important meth-
odological diversity is in construction supply chain risk management
studies. It is essential to match the research topic to the right approach,
considering both their strengths and limitations.
The management of construction projects involves a complex array
of decisions, coordinating stakeholders, and resource allocation, all
while navigating the uncertainties and risks inherent to the eld [76].
Viewing traditional construction supply chain risk management
methods through this managerial perspective brings their limitations
into sharper focus. At the heart of management is the capacity to apply
insights and decisions consistently across multiple projects. Traditional
approaches, such as case studies or site visits, although providing
detailed analysis, often lack the breadth needed to address risks in varied
project contexts [98]. This means that managers might nd it chal-
lenging to apply insights from one specic study or site visit to other
distinct projects without potential errors.
Time is another major consideration. Construction managers operate
under strict schedules and deadlines. Methods that are comprehensive
and repetitive, like systematic literature reviews or grounded theory,
may not t seamlessly into the swift decision-making required in con-
struction environments. While these methods maintain academic
integrity, they might seem too theoretical and not immediately appli-
cable in the practical setting of a construction project [51]. Effective
communication and stakeholder management also play a pivotal role.
The construction sector involves various participants, ranging from
suppliers and contractors to clients and local authorities. Organizing
group discussions or workshops to assess risks not only requires efcient
planning but also effective communication skills to ensure all viewpoints
are considered. If traditional collaborative methods aren’t managed
effectively, louder voices might overshadow others, potentially side-
lining valuable insights. This can even lead to disagreements or tensions
within the team, undermining the very essence of collective risk eval-
uation [117].
From a managerial standpoint, balancing data-based insights with
real-world observations presents another challenge. Techniques like
simulations or statistical analyses, which provide measurable risk in-
sights, may appear too theoretical for a manager dealing with immediate
on-site issues. Managers often grapple with the challenge of integrating
data-driven predictions with the practical complexities encountered in
construction settings. Furthermore, while tools like the AHP provide
systematic ways to rank risks, they may not always align with the dy-
namic nature of construction management [118,119]. Construction
projects are ever evolving, with changing priorities and unforeseen
challenges. As such, managers might need more adaptive tools than
what structured methods offer. The inherent subjectivity of some of
these tools might also amplify managerial biases, affecting accurate risk
perception.
Hence, while traditional methods of construction supply chain risk
management have their strengths, they come with several challenges
when assessed from a managerial viewpoint. These range from the need
for broader applicability and quick decision-making to effective stake-
holder communication and the integration of data-driven insights with
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
16
real-world experiences. Tackling these challenges necessitates both
method adaptation and a comprehensive grasp of managerial intricacies
within the dynamic world of construction.
RQ.4 What are the impacts of AI in the management of risks within
the construction supply chain?
AI has revolutionized CSCRM, transforming the process of identi-
fying, assessing, and mitigating risks in the construction supply chain.
With its various branches, AI has fundamentally reshaped the way risks
are managed in this industry. In the eld of CSCRM, there are various AI
techniques that play a crucial role. Among them, Deep Learning stands
out as a powerful tool for real-time risk assessment. By processing
extensive amounts of supply chain data with exceptional precision, it
acts as a vigilant guardian, continuously monitoring the landscape for
anomalies and threats. Another technique called Natural Language
Processing (NLP) leverages its linguistic nesse to extract insights from
textual data, ensuring that no information is overlooked and empow-
ering decision-makers to better understand and manage risks [120].
Additionally, Fuzzy Set Theory brings nuance to risk assessment by
recognizing that risks exist on a spectrum with varying degrees of un-
certainty. This allows for more accurate prioritization and the devel-
opment of targeted strategies, enabling effective handling of complex
scenarios [121]. The Machine Learning toolbox offers tools like decision
trees and random forests, which provide adaptability in modeling
diverse risks and ensuring preparedness. Genetic algorithms contribute
to optimization efforts by nding cost-effective strategies through
leveraging non-linear relationships. Bayesian networks facilitate accu-
rate modeling of risk interdependencies and cascading impacts. When
combined, these advanced techniques empower CSCRM with real-time
monitoring capabilities, nuanced assessment methods, optimization
potentialities, predictive abilities, and adaptable modeling capabilities
all working towards holistic risk minimization within the construction
supply chain domain [122].
In the eld of CSCRM, AI-driven algorithms for route optimization
are invaluable tools in minimizing transportation risks. These algo-
rithms deem various factors like real-time trafc conditions, weather
forecasts, and historical incident data to determine the most efcient
delivery routes. By doing so, they not only decrease the likelihood of
disruptions in the supply chain caused by delays but also contribute to
cost savings by reducing fuel consumption and travel time [123]. For
instance, a construction materials supplier utilizes AI-powered route
optimization to ensure timely delivery of essential materials to different
project sites, thereby mitigating the risk of construction delays stemmed
from material shortages [124].
AI has revolutionized demand forecasting in CSCRM. By analyzing
vast amounts of data, including historical sales data, market trends,
economic indicators, and even external factors like weather patterns, AI-
powered demand forecasting models provide a more precise and
adaptable understanding of future demand for construction pro-
fessionals [125]. This level of accuracy allows supply chains to quickly
adjust to changing customer needs, reducing the risks associated with
overstocking or understocking. For instance, a construction project
manager can rely on AI-driven demand forecasting to ensure the timely
procurement of the right quantity of materials, avoiding nancial losses
caused by excess inventory or project delays due to material shortages.
AI also plays a signicant role in supplier relationship management
within CSCRM. Through AI-driven supplier risk assessment models,
organizations can continuously monitor the performance of suppliers
across various parameters such as delivery punctuality, product quality,
nancial stability, and even geopolitical factors. By identifying potential
risks at an early stage, these models allow organizations to proactively
address any concerns with suppliers before they turn into major dis-
ruptions. For example, a construction rm that utilizes an AI-based
supplier risk assessment system can promptly detect deteriorating
nancial conditions of a key supplier. This early insight enables the
construction company to explore alternative suppliers, helping prevent
potential delays and cost overruns in projects [126].
Collaborative robots, often referred to as cobots, equipped with AI
capabilities are a practical application of AI in mitigating risks associ-
ated with CSCRM. In warehouse and manufacturing settings, cobots
handle repetitive and labor-intensive tasks, leading to increased ef-
ciency while reducing the likelihood of human errors [127]. By allowing
human workers to focus on more complex and valuable activities, cobots
not only enhance productivity but also contribute to safer working
conditions. For instance, a construction materials warehouse that em-
ploys AI-driven cobots for inventory management can ensure accurate
sorting and availability of materials when needed, reducing the risk of
delays due to misplaced or unavailable inventory [128]. Furthermore,
AI offers valuable support in nancial risk management within CSCRM.
AI-driven credit risk assessment models play a vital role in evaluating
the nancial stability of supply chain partners, such as suppliers and
customers. These models analyze various nancial data, payment his-
tories, market conditions, and economic indicators to determine the
creditworthiness of these entities. This helps organizations reduce the
risk of late payments, defaults, or disruptions in their supply chain
[129]. A construction project developer can utilize AI-powered credit
risk assessment to assess the nancial health of subcontractors, ensuring
that they have the capacity to meet their contractual obligations,
thereby minimizing project delays and cost overruns.
Furthermore, by combining AI and Internet of Things (IoT) tech-
nologies in CSCRM, supply chain assets can be monitored in real-time.
IoT sensors attached to goods and equipment continuously send data
to AI systems, allowing for immediate identication of any anomalies or
disruptions. This live visibility greatly improves risk assessment and
response capabilities. For instance, a construction equipment rental
company could use IoT sensors and AI to monitor the condition of their
machinery. If a breakdown is predicted, AI can schedule maintenance
ahead of time, reducing the risk of expensive equipment downtime at
construction sites. AI adoption in the construction supply chain is
bringing signicant benets to professionals. AI-powered decision sup-
port systems offer powerful analysis of complex scenarios and provide
recommendations for risk reduction strategies. These systems assist
construction project managers in making informed decisions that
effectively balance risk and reward [121]. For example, they can eval-
uate multiple procurement options for critical materials by considering
factors such as cost, lead time, and supplier reliability. Ultimately, these
AI-driven systems help minimize risks and optimize overall project
performance.
Moreover, combining blockchain technology (BCT) and Radio Fre-
que-ncy Identication (RFID) technology with AI in CSCRM can signif-
icantly improve its effectiveness. This integrated approach offers a
comprehensive solution for mitigating risks in the construction industry.
RFID technology plays a crucial role in the construction supply chain by
providing real-time asset tracking and monitoring. By attaching RFID
tags to materials, equipment, or inventory, data can be transmitted to AI
systems that enable quick identication of asset locations and conditions
[130]. This real-time visibility helps prevent loss of assets and improves
risk assessment and response capabilities. For instance, AI algorithms
can analyze RFID data to anticipate potential delays or disruptions by
tracking the movement and availability of crucial materials or equip-
ment. This proactive approach empowers supply chain professionals to
take timely action in mitigating risks [131].
Blockchain technology plays a crucial role in promoting trans-
parency and trust within the construction supply chain. Its secure and
unmodiable ledger ensures the integrity of transactions and documents
related to contractual agreements, payments, and materials tracking
[132,133]. By combining blockchain with AI, risk management is
further improved through real-time data validation and verication,
including RFID data. This powerful combination reduces the risk of
fraud and disputes while also enabling proactive risk detection by
continuously monitoring critical asset status and location [134].
To summarize, the impact of AI on CSCRM goes beyond traditional
risk management practices. It improves logistics, enhances demand
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
17
forecasting, supports supplier relationship management, automates
tasks, and aids in nancial risk assessment. By combining AI tools with
IoT and decision support systems, a comprehensive approach to CSCRM
is achieved. This helps construction supply chains become more agile,
efcient, and resilient in the face of uncertainties. This evolution is not
just a technological shift; it represents a fundamental change in how
organizations understand, assess, and mitigate risks in the dynamic
construction supply chain landscape. Ultimately, this contributes to the
success of construction projects and satises stakeholders. By inte-
grating RFID, BCT, and AI into CSCRM, a comprehensive risk manage-
ment approach is achieved. RFID technology enables real-time asset
tracking and monitoring, while blockchain enhances transparency and
trust by safeguarding crucial data.
Research Gaps and Future Directions
In the following section, we elucidate various gaps currently evident
in the domain of construction supply chain risk management. Alongside
these gaps, potential future directions are presented, highlighting ave-
nues for innovation and enhancement in the eld. The aim is to provide
readers with a clear perspective on areas ripe for exploration and further
research, anchoring the discussion in both current challenges and pro-
spective solutions:
1. Based on RQ.1.3, signicant growth has been observed in global
CSCRM research, but as much attention doesn’t seem to be received
by some countries, such as Norway and Bhutan, despite many pub-
lications. It should be investigated why the work from these coun-
tries isn’t cited as often. Perhaps it’s about the topics being studied or
the way they’re presented. A role might also be played by local
challenges, construction methods, or nancial issues. By having this
understood, it can be ensured that research from all regions is given a
fair consideration and is included in the broader CSCRM discussion.
2. Based on RQ.2, the eld of CSCRM has a signicant gap in its overall
approach, lacking proactive strategies. Traditionally, the focus has
been on identifying, assessing, and analyzing risks in the industry.
Over time, additional phases such as risk allocation and control have
been introduced to address risks reactively after they occur. How-
ever, recent emphasis on risk recovery highlights the need for a more
proactive approach. Proactive risk management involves antici-
pating and preventing risks rather than just reacting to them [135]. It
is crucial to prioritize early recognition of potential risks through
comprehensive analysis. Currently, there is inconsistent prioritiza-
tion of risk identication in CSCRM. Furthermore, there needs to be
clear responsibilities and resource allocation for managing identied
risks through proper risk allocation practices. Given the complexities
involved across various domains like operational, managerial,
nancial, and legal aspects in construction supply chain manage-
ment, proactive risk management becomes essential. This includes
navigating micro-level operational challenges as well as macro-scale
external factors or disruption risks like political shifts and nancial
uctuations. Fostering a proactive risk management culture within
the construction supply chain is crucial for project resilience and
stakeholder satisfaction in this ever-evolving landscape of con-
struction. This entails not only identifying and assessing risks but
also anticipating them ahe-ad of time to ensure succe-ssful outcomes.
3. Based on RQ.3, to improve empirical research in the eld, it is
essential to go beyond relying solely on case studies. While these
studies offer valuable insights into specic contexts, they may lack
generalizability. Exploring alternative methodologies such as
ethnographic or longitudinal studies [136,137], and incorporating
comparative analyses, can help bridge this gap by providing deeper
analysis while maintaining broader applicability. Additionally, it is
important to consider the limitations of relying heavily on surveys,
which account for approximately 33.59% of current research
methods. While surveys provide valuable perspectives, concerns
arise due to biases like response rates and selection issues. To address
these concerns, innovative solutions such as Delphi methods (with
iterative questioning and consensus-driven approach) or mixed
methods research (combining quantitative and qualitative data) can
be explored. Furthermore, advanced analytical techniques like Social
Network Analysis (SNA) and Monte Carlo simulations offer depth
and complexity when studying CSCRM. However, they also have
requirements for substantial data and may be limited by certain as-
sumptions. Renements in these techniques could include hybrid
methods that combine elements of different approaches or utilizing
emerging technologies like blockchain for data sourcing [138], and
augmented reality (AR) for enhanced visualization [139]. These re-
nements will enable researchers to gain more comprehensive in-
sights that are nuanced and actionable in CSCRM research.
4. Based on RQ.4, in the realm of AI, two methodologies have emerged
as particularly impactful: Reinforcement Learning (RL) and
transformer-based models like BERT, GPT, and T5. RL provides a
structured approach to decision making in dynamic situations.
Through trial and error, an agent learns the best actions to take in
specic states to maximize long term rewards [140]. DQNs and PPO
are notable algorithms in this eld, demonstrating effectiveness
across various applications. For CSCRM, which involves multiple
variables such as material availability, labor shortages, and logistical
disruptions, RL can be invaluable. For example, if there is potential
for a material delay due to geopolitical events agged by news
sources, RL algorithms can suggest optimal procurement or sched-
uling adjustments to minimize disruptions. RL not only enables re-
action but also allows strategic planning for future risks.
Conversely, transformer models such as BERT (Bidirectional
Encoder Representations from Transformer), XLNet, and DistilBERT
excel at processing and understanding vast amounts of textual in-
formation [141]. These architectures can decipher nuanced context
in data from rich sources like news and social media. While other
industries, such as agricultural and healthcare sectors have started
integrating these AI tools into their supply chain risk management
using [142,143], the construction industry lags behind. Integrating
RL’s decision-making capabilities with the text processing strength
of transformer models could offer a novel approach to CSCRM. This
integration would enable proactive anticipation and mitigation of
risks by leveraging real-time insights from global news and social
media sources. Given the potential, there is an academic opportunity
to explore how RL and transformer-based models can be applied
together in CSCRM. Taking this research path could shift the indus-
try’s focus from a reactive approach to a more proactive and
forward-thinking risk management strategy.
4. Conclusion
The current review of the systematic literature and bibliometric
analysis on construction supply chain risk management (CSCRM) from
1999 to 2023 presents several noteworthy ndings and unique contri-
butions to the eld, emphasizing the critical evolution of risk manage-
ment practices within construction supply chains.
One of the most surprising ndings from the review is the substantial
increase in the adoption of articial intelligence (AI) methods for
CSCRM, particularly noted since 2016. This trend signies an important
shift in risk management practices, from traditional techniques such as
surveys, case studies, and statistical tools, to more advanced and pre-
dictive AI methodologies. The inclusion of AI in CSCRM underscores the
potential for signicantly improved risk identication, assessment,
analysis, allocation, prioritization, and recovery processes within the
construction supply chain. The rapid escalation in AI applications,
despite the persistent utilization of traditional approaches, reveals an
industry willingness to adopt technological transformation, aiming to
leverage AI for enhanced risk management capabilities. Additionally,
this study reveals a signicant shift in the evolution of risk management
within the construction supply chain. Initially centered on reactive
measures, the focus has now progressed towards the recovery phase,
M. Baghalzadeh Shishehgarkhaneh et al.
Automation in Construction 162 (2024) 105396
18
employing a proactive approach. This emphasizes the growing impor-
tance of resilience in the eld. The uniqueness of this review study is its
comprehensive analysis covering a 25-year period, offering a holistic
overview of the evolution and current state of CSCRM. The systematic
approach to literature review and bibliometric analysis provides a rich
combination of theories, methods, and emerging technologies in the
eld, along with critical risk management approaches and publication
trends. By highlighting the rising integration of AI with traditional risk
management techniques, the review not only captures the historical
progression of CSCRM but also sheds light on future directions and op-
portunities for research and practice. The identication of inuential
authors, journals, and their main contributions (Table 4), and collabo-
rative networks further contributes to understanding the developmental
landscape of CSCRM research and practice.
Enriching the eld, this paper studies and integrates existing
knowledge on construction supply chain risk management, serving as a
vital step for both academic research and practical application. It offers a
critical resource for researchers, industry professionals, and policy-
makers by outlining a structured synthesis of key research themes,
notable contributions, structured risk taxonomy, and identied gaps.
This foundation encourages further investigation into previously unex-
plored areas such as the widespread application of AI in CSCRM and
addresses the gaps that have been identied. Hence, the review paper
not only documents the state-of-the-art in CSCRM but also highlights the
dynamic nature of the eld, with emerging trends and technologies
shaping the future of construction supply chain risk management. The
unique contributions and the comprehensive scope of this review un-
derscore its signicance and justify its publication, providing a vital
reference point for advancing CSCRM research and practice.
While this study provides valuable insights, it is important to
acknowledge its limitations. The analysis heavily relies on Scopus, a
widely recognized database. A comprehensive review would encompass
other sources such as WOS, Google Scholar, and various global data-
bases. Additionally, the study focuses solely on English peer reviewed
articles and overlooks conference proceedings and non-English contri-
butions. Future research should consider exploring these broader ave-
nues to provide a more comprehensive understanding of the CSCRM
research landscape.
CRediT authorship contribution statement
Milad Baghalzadeh Shishehgarkhaneh: Writing – review & edit-
ing, Writing – original draft, Visualization, Formal analysis, Data cura-
tion, Conceptualization. Robert C. Moehler: Writing – review &
editing, Supervision, Resources, Project administration, Investigation,
Funding acquisition, Conceptualization. Yihai Fang: Writing – review &
editing, Supervision, Resources, Project administration, Investigation,
Data curation. Hamed Aboutorab: Validation, Software, Methodology,
Data curation. Amer A. Hijazi: Visualization, Validation, Supervision,
Software, Formal analysis, Data curation.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
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