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Article

Uncovering the Research Hotspots in Supply Chain Risk Management from 2004 to 2023: A Bibliometric Analysis

by
Tianyi Ding
and
Zongsheng Huang
*
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(12), 5261; https://doi.org/10.3390/su16125261
Submission received: 16 May 2024 / Revised: 15 June 2024 / Accepted: 16 June 2024 / Published: 20 June 2024
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
As globalization deepens, factors such as the COVID-19 pandemic and geopolitical tensions have intricately complexified supply chain risks, underscoring the escalating significance of adept risk management. This study elucidates the evolution, pivotal research foci, and emergent trends in supply chain risk management over the past two decades through a multifaceted lens. Utilizing bibliometric tools CiteSpace and HistCite, we dissected the historical contours, dynamic topics, and novel trends within this domain. Our findings reveal a sustained fervor in research activity, marked by extensive scientific collaboration over the past 20 years. Distinct research hotspots have surfaced intermittently, featuring 20 domains, 62 keywords, and 60 citation bursts. Keyword clustering identified seven nascent research subfields, including stochastic planning, game theory, and risk management strategies. Furthermore, reference clustering pinpointed five contemporary focal areas: robust optimization, supply chain resilience, blockchain technology, supply chain finance, and Industry 4.0. This review delineates the scholarly landscape from 2004 to 2023, uncovering the latest research hotspots and developmental trajectories in supply chain risk management through a bibliometric analysis.

1. Introduction

Supply chain risk management (SCRM) entails identifying, evaluating, and mitigating potential disruptions in the supply chain to reduce uncertainties and production interruptions. Effective SCRM enhances the stability and sustainability of supply chains, boosting the overall competitiveness of enterprises. Historically, the importance of SCRM has grown in tandem with the advances in globalization, evolving from simple internal risk controls to the sophisticated global frameworks necessary for managing complex, interdependent networks.
Before the 1990s, research on supply chain risks primarily focused on internal enterprise management, such as inventory control, production planning, and logistics optimization [1]. The emphasis was on mitigating uncertainties within the production process. From the 1990s through the early 21st century, as the global division of labor intensified, the scope of supply chain risks expanded to encompass entire supply systems. Researchers began to explore broader issues, including partnerships, coordination mechanisms, and risk-sharing within supply chains. This period also saw the emergence of foundational SCRM theories and practices, such as disruption risk management, supplier evaluation, and contract design [2].
The advent of the 21st century marked a pivotal shift as globalization peaked, magnifying the critical nature of SCRM. Research expanded to cover not only traditional risk sharing and disruption management [3,4] but also the impacts of geopolitics, regional conflicts, and environmental changes on global supply chains [5,6]. Additionally, the rise of digital technologies such as big data, the Internet of Things, artificial intelligence, and cybersecurity introduced new focal areas in SCRM.
The outbreak of COVID-19 in 2020 underscored the vital role of SCRM against the backdrop of a global crisis. Border closures and international transport restrictions highlighted the fragility of global supply chains, catalyzing a surge in research aimed at enhancing supply chain resilience under ongoing pandemic conditions. This body of work has contributed to understanding how to design supply chains that are robust against a range of emerging risks.
Today, the historical trajectory of SCRM research reflects its evolution from focusing on internal risks within companies to addressing comprehensive risks across global networks, incorporating technological innovations and adapting to continually changing market conditions [7]. Over the past two decades, the Web of Science database has indexed over 10,000 publications related to “supply chain risk”, spanning thousands of journals and a multitude of disciplines, such as business management and information science. The vast scale of the research makes the manual analysis of trends and dynamics challenging. Employing bibliometric methods allows for a more efficient and comprehensive exploration of the historical contours, research characteristics, and development trends within this field. Our research is structured around several pivotal questions:
(1)
How have publication trends within SCRM evolved over time?
(2)
What are the dominant topics currently shaping SCRM research, and how have they impacted the field?
(3)
What cutting-edge areas are emerging in SCRM, and what future trajectories might the field pursue?
By addressing these questions, our study seeks to bridge existing gaps in understanding the dynamic trends within SCRM. Through this extensive review, covering the period from 2004 to 2023, we provide a detailed examination of the evolving scholarly landscape in SCRM, highlighting the most recent research hotspots and developmental trajectories.
In this paper, we employ sophisticated bibliometric tools, CiteSpace (version 5.8 R3) and HistCite Pro 2.1, to conduct a comprehensive literature analysis in the field of supply chain risk management (SCRM). Our study commences with the meticulous collection and screening of a significant body of literature, establishing a foundation for an in-depth exploration of the evolution of SCRM. This approach is particularly relevant in the current global context, where challenges such as the COVID-19 pandemic and geopolitical tensions have significantly complicated supply chain risks, emphasizing the critical need for proficient risk management strategies.
The organization of this paper is as follows: Section 2 outlines the methods of data collection and the analytical capabilities of the bibliometric tools used. Section 3 delves into the analysis of our findings, presenting a detailed breakdown of publication trends, keyword frequencies, and citation patterns. The paper concludes with Section 4, which summarizes our findings and discusses future research trends in SCRM, suggesting potential directions for further investigation.

2. Method

2.1. Data Collection and Statistics

The Web of Science Core Collection (WOS) from Thomson Reuters houses over 12,000 prestigious academic journals, widely acknowledged as significant by the global scholarly community. This study utilized WOS as the primary data repository. We conducted a comprehensive search query, TS = ((“Risk” OR “Vulnerability” OR “Disruption” OR “Disruptions” OR “Resilience”) AND (“Supply chain”)), spanning the years 2004 to 2023. This search yielded a total of 12,677 scholarly records. Additionally, we systematically gathered original data concerning countries/regions, institutions, journals, authors, article types, etc.

2.2. Bibliometric Analysis Tools

2.2.1. CiteSpace

CiteSpace (version 5.8 R3) is a sophisticated software tool designed for visual document analysis, primarily used in the fields of scientometrics and scientific knowledge mapping. It offers a dynamic graphical representation of the structure, patterns, and distribution of scientific knowledge. Key functionalities of CiteSpace include foundational document analysis, cluster analysis, and burst detection, which help identify emerging trends. Additionally, it facilitates collaboration network analysis, topic evolution tracking, and visual representations of citation networks. The software also supports subject-level analysis, among other features, enabling a comprehensive examination of complex research landscapes.
When an article in a research field is imported into CiteSpace software in the form of a data set, these synergies and scientific concepts can be visualized into a co-existing network. CiteSpace uses color-coded nodes and edges to distinguish the merged network and allocate its colors in the data set every year. The color of the edge of the network indicates the year in which the link was first created. Nodes are composed of “tree wheels” of different colors, and their thickness represents the number of common occurrences in a given year. The red ring indicates a citation explosion in a certain year, that is, a surge in the number of references in that year. The purple ring is used to indicate the degree of inter-node centrality. A node with a high intermediate center is meaningful because it connects one node to another. CiteSpace provides three clustering algorithms based on titles, abstracts, and keywords, which can classify publications into conceptual clusters with different research characteristics. According to the setting of the slice, the cluster mapping reflects the changes in conceptual clustering in different periods. In addition, the timeline mapping can clearly reflect the rise and fall time of a cluster and the nodes associated with other clusters.

2.2.2. HistCite

HistCite Pro 2.1 software can draw this numerical relationship diagram and extract the most interesting documents, allowing us to intuitively see which documents are cited the most. HistCite scores articles using the local citation score (LCS) and the global citation score (GCS). LCS refers to the number of citations of the research in the software, and GCS refers to the number of citations in the WOS database.
We imported research articles in supply chain risk (9713 articles) into Hiscite Pro 2.1, set the “restriction” to 30, kept the default value for other settings, and selected “make charts” to draw the context of the field of supply chain risk research and quickly locate important literature.

3. Results

3.1. The Historical Features of the Supply Chain Risk Literature

3.1.1. Distribution of Publications

This study retrieved a total of 12,677 papers related to supply chain risks. Among these publications, research articles account for the vast majority, with a total of 11,764 articles, and there are 913 review articles. These articles systematically sort out and review the research on supply chain risks. These papers have a total of 28,880 participating authors, are from 7568 participating institutions, and are published in 2210 journals in 201 scientific categories (Table 1). They are highly interdisciplinary and comprehensive.
Annual research publications are depicted in Figure 1a. The data reveal that 35 and 47 papers were published in 2004 and 2005, respectively, indicating that supply chain risk management initially garnered limited attention at the dawn of the 21st century. This can be attributed to the fact that the supply chain had not yet fully embraced deep globalization. Analyzing the trajectory of knowledge accumulation and developmental trends, the publication volume in supply chain risk research has consistently increased over the years. Notably, after 2008, the pace of publication accelerated significantly, culminating in a peak in 2023. This trend underscores the growing significance of supply chain risk management, which, propelled by the deepening of globalization and advances in information technology, has emerged as a focal area of research, drawing increasing interest from scholars and institutions worldwide.
In addition, judging from the journal distribution of research results, the top three journals are “Sustainability” (569 articles), “International Journal of Production Economics” (492 articles), and “International Journal of Production Research” (463 articles). This shows that research on supply chain risk management is more popular in these journals. When considering submitting a manuscript, researchers can refer to the top 20 journals with the most results in Figure 1b and select journals that suit their research direction and level to submit, which can also increase the probability of publishing their research results. The journals here are mainly related to operations management, logistics, and supply chain management, and there are also some journals from special industries, such as “Food Control”, which show that supply chain risk management also has important value in the field of food safety.

3.1.2. The Veins of Supply Chain Risk Research Field

The citation co-citation graph depicted in Figure 2 illustrates the relationships among the literature in the field of supply chain risk management over the past two decades. The network comprises 1999 nodes and 9826 links, indicating a rich tapestry of interconnections among the studies within this research area. In the earlier literature, spanning from 2004 to 2013, nodes marked in gray are particularly noteworthy. These nodes, characterized by their dense clustering and robust interconnections, form the foundational roots of the field. They have fueled the ongoing development of the discipline, embodying the essential research and core theories that focus on fundamental aspects, such as risk identification, assessment, and management. These foundational studies not only establish a solid base for subsequent research but also serve as crucial connectors bridging various research themes and areas.
In the mid-term phase (2014–2017), the nodes marked in blue became increasingly dispersed, forming the primary branches of research within the field. This period marked a shift towards more specialized and segmented areas of study, as scholars from diverse perspectives further enhanced the field’s diversity and richness.
During the later phase of research (2018–2023), these nodes matured into denser clusters, illustrating a concentration and differentiation within the research field. The focus of supply chain risk management research transitioned from theoretical discussions to practical applications, with increased attention on the real-world impacts of risks, response strategies, and the assessment of outcomes. Notably, significant contributions emerged from researchers such as Ivanov D., Hosseini S., Dolgui A., van Hoek R., El Baz J., and Pettit T.J. These authors’ papers, particularly noted for their high and intermediate co-citation counts—463, 352, 308, 305, 250, 245, 242, 242, 234, and 230, respectively—demonstrate central roles in the network with prominent betweenness centrality (indicated by a purple ring).
Ivanov D.’s series of studies delve into managing supply chain disruptions and enhancing resilience, offering crucial insights for maintaining stability in crises. Hosseini S. contributes significantly to the identification and assessment of supply chain risks, providing essential decision-making support. Other scholars, such as Dolgui A., van Hoek R., El Baz J., and Pettit T.J., have enriched both the theoretical framework and practical applications of supply chain risk management from varied angles. Their findings have significantly influenced the evolution of theory and practice in the field, marking them as landmark contributions in the realm of supply chain risk research.

3.1.3. Scientific Cooperation

As depicted in Figure 3, the extensive array of nodes and dense network of links highlight the significant level of scientific collaboration across countries, institutions, and authors within this field. Specifically, the national cooperation network, illustrated in Figure 3a, includes 151 nodes and 1641 connecting lines. This robust connectivity underscores the frequency and depth of cross-country collaborations in scientific research. Prominent countries such as China, the United States, Germany, the United Kingdom, and Australia dominate in terms of node scale, reflecting their strong research capabilities and influential roles across multiple scientific domains.
The network of institutional cooperation, comprising 641 nodes and 802 connecting lines, further demonstrates the extensive breadth and depth of collaboration between institutions. This connectivity not only facilitates the sharing of knowledge and resources but also enhances the overall impact and innovation potential of the research conducted in these networks.
Figure 3b displays the network of institutional cooperation. The diagram reveals the formation of regional cooperative groups among various institutions. Notably, Hong Kong Polytechnic University, the Chinese Academy of Sciences, Tianjin University, the Iran University of Science & Technology, the Indian Institute of Management, the University of California System, and the State University System of Florida have established distinct advantages in the field of supply chain risk management. These institutions play a pivotal role in leading collaborative research efforts among diverse organizations.
The author collaboration chart, as depicted in Figure 3c, illustrates the extensive collaborative relationships among researchers in the field of supply chain risk management. Prominent scholars such as Choi, Tsan-ming; Ivanov, Dmitry; Paul, Sanjoy Kumar; Dolgui, Alexandre; and Govindan, Kannan are highlighted for their prolific contributions to this area. The dense interconnections among these authors indicate significant scientific cooperation, facilitating the sharing of resources and exchange of ideas. Such collaborative efforts not only enhance the openness and cooperative nature of scientific research but also enable researchers to collectively tackle challenges in supply chain risk management. As this field continues to evolve and expand, the integration of more researchers into this network is crucial. In the future, as additional scholars enter this field, the ties among them are expected to grow even stronger, further advancing the discipline.

3.2. Variation of the Most Active Topics

3.2.1. Subject Category Burst

We have analyzed citation bursts across nearly two decades of the academic literature, spanning from 2004 to 2023. This analysis encompasses dynamic shifts across 201 related subject areas, revealing 60 significant citation bursts. A blue line in the timeline graphically pinpoints the precise moments when citations in each subject area surged, while red line segments vividly illustrate the duration of these bursts, marking both their start and end years.
From the data visualized in Figure 4, we identified the top 50 categories that exhibited the highest burst intensities during various periods. The VOSviewer visualization employs varying node sizes to indicate the intensity of these bursts, with larger nodes denoting higher intensities. For instance, the subject category of ENGINEERING, MANUFACTURING stands out as a large node for the period between 2014 and 2016, reflecting its substantial burst intensity of 28.79.
Moreover, over time, the nodes and connections in the VOSviewer diagram have become increasingly diverse, signaling a trend of interdisciplinary integration and the emergence of multiple research hotspots. Significant subject areas such as BUSINESS (2005–2007), INFORMATION SCIENCE and LIBRARY SCIENCE (2008–2010), MECHANICS (2013–2019), and OCEANOGRAPHY (2002–2023) show relative increases in activity and prominence during their respective time frames. Notably, from 2023 onwards, 20 new emerging disciplines have been identified. These disciplines appear as prominent and sizable new nodes in the graph, with TELECOMMUNICATIONS, CONSTRUCTION and BUILDING TECHNOLOGY, and OCEANOGRAPHY being the top three emerging disciplines, clearly distinguished by their node sizes.

3.2.2. Keywords Burst

To more precisely identify core research hotspots in the field of supply chain risk, we utilized the burst pattern of keywords over the period from 2004 to 2023. A total of 980 keywords displayed significant bursts at various times, with Figure 5 highlighting the top 50 keywords that exhibited the highest outbreak intensity. This analysis provides insights into the dynamic evolution and central issues within the field of supply chain risk. Here is a detailed breakdown:
The keyword “supply chain management” held a predominant position throughout the field with an exceptionally high burst intensity of 101.3 from 2004 to 2013. This indicates that in the context of globalization and intensified market competition, companies have been continually optimizing their supply chain management strategies to mitigate potential risks. Consequently, a substantial body of research has focused on various facets of supply chain management, including supplier selection, inventory management, and logistics coordination.
Additionally, the keyword “risk management” also showed a significant burst intensity of 33.92 from 2004 to 2014. This suggests that scholars have extensively discussed and proposed various risk management strategies and methods from multiple perspectives, offering valuable insights and recommendations.
In more recent developments, the keyword “artificial intelligence” has emerged with a burst intensity of 19.81, reflecting the growing impact of technologies such as big data and machine learning. This trend suggests that artificial intelligence holds considerable potential value and broad application prospects within the field of supply chain risks, positioning it as a burgeoning research focus.
Moreover, as of 2023, 62 keywords continue to exhibit high burst intensities, signaling ongoing, critical challenges that urgently need addressing in supply chain risk management. For instance, the keyword “COVID-19 pandemic” between 2021 and 2023 had a burst intensity of 22.3, highlighting the significant impacts of the pandemic on supply chains and the broader implications for global public health events on supply chain risk management.
Emerging keywords such as “closed-loop supply chain” (2020–2023, burst intensity of 8.13) and “industry 4.0” (2021–2023, burst intensity of 7.9) underscore the growing relevance of sustainability practices and advanced manufacturing technologies in addressing contemporary supply chain challenges.
This analysis shows that while it is important to focus on the most explosive keywords, it is equally critical to broaden our perspective to include emerging terms. These keywords represent the evolving research hotspots and cutting-edge technologies, offering valuable insights into the direction of future studies in the field.

3.2.3. Reference Burst

Using the analysis and calculation features provided by VOSviewer, we identified a total of 1400 groundbreaking articles published between 2004 and 2023. Table 2 presents a summary of the top 30 most frequently cited references during this period, highlighting key contributions and seminal works that have significantly influenced the field.
The analysis and computation modules of VOSviewer have revealed significant scholarly contributions in the field of SCRM between 2004 and 2023. Ho et al. stands out with the highest citation burst rate [7]. This seminal work offers an extensive review of the last decade’s literature in SCRM, meticulously detailing the progress in defining supply chain risks, categorizing risk types and factors, and developing risk management and mitigation strategies. The authors not only delve into the depth and breadth of existing studies but also illuminate the intrinsic connections and distinctions among them. Furthermore, they identify research gaps and potential future directions through a thorough analysis of SCRM literature.
Heckmann et al. has garnered substantial attention since its publication, demonstrating a burst intensity of 85.55 and maintaining popularity for five years [8]. This paper conducts a profound analysis of the complexities and uncertainties facing economic systems today, advocating for informed decision-making grounded in robust risk analysis, control, and mitigation. It highlights the recognition and in-depth understanding of risk considerations across finance, insurance, crisis management, and healthcare. The paper underscores the frequent and severe consequences of supply chain disruptions, thereby emphasizing the strategic importance of risk management within supply chains. Heckmann et al.’s contribution is notable for its comprehensive review and deep analysis of supply chain risk management as well as its emphasis on applying interdisciplinary approaches in this field.
The paper by Takahashi et al. [9], with a burst intensity of 62.14 from 2016 to 2020, broadens the understanding of factors that enhance a firm’s resilience to supply chain disruptions. The study reveals that merely reacting to disruptions is insufficient; firms must also possess capabilities to reallocate resources or improve their risk management infrastructure to bolster resilience. It differentiates between high-impact disruptions, where resource reallocation is crucial, and low-impact disruptions, where supply chain orientation and risk management infrastructure are effective in tandem.
Recent advancements in SCRM are reflected in the production of 143 high-profile articles from 2023 alone. Table 3 lists the top 20 articles, categorized by strength index, including two reviews and eighteen research articles. The reviews comprehensively summarize the historical development and current challenges in the supply chain risk field and speculate on future trends. The eighteen research articles offer fresh insights and strategies on specific implementation issues in supply chain risk from various perspectives. Notably, all these articles entered a citation burst phase immediately upon publication or the following year, gaining widespread influence and recognition within the academic community.

3.3. Emerging Trends and New Developments

3.3.1. The Temporal Variation of Keyword Clusters

The keyword analysis across the past two decades reveals intricate internal connections, prompting us to segment the study into four five-year phases to track the dynamic evolution within the field. The corresponding keyword clustering snapshots for each phase are illustrated in Figure 6.
In the first phase (2004–2008), depicted in Figure 6a, we recorded 356 papers that coalesced into seven clusters, including stochastic programming, game theory, and supply chain risk. During this initial period, the introduction of methodologies such as stochastic programming and game theory introduced robust tools for a quantitative analysis and decision support in supply chain risk management.
The second phase (2009–2013) saw a substantial increase to 1056 papers, as shown in Figure 6b. The emerging clusters during this period included supply chain management, food safety, and continued emphasis on stochastic programming. Notably, the focus on food safety highlighted the sector-specific risks associated with supply chains, marking it as a significant area of interest within the broader field of supply chain risk.
By the third clustering period (2014–2018), the number of publications surged to 2524, with Figure 6c highlighting dominant clusters, such as game theory and supply chain risk management. This resurgence of interest in game theory underscores its ongoing relevance and utility in addressing complex supply chain challenges.
The fourth phase (2019–2023) represented the most extensive and in-depth research period, with a staggering 8741 papers, forming eight clusters, including supply chain coordination, supply chain resilience, and food safety, as detailed in Figure 6d. This period underscored a significant focus on enhancing supply chain resilience and coordination to mitigate a wide array of potential risks.
The emerging literature from these clusters primarily revolves around optimizing supply chain coordination mechanisms and improving resilience. Specifically, 149 and 139 articles, respectively, have focused on these aspects, discussing practical approaches to enhancing supply chain resilience against various risks. This analysis not only illustrates the growing complexity and depth of research in supply chain risk but also highlights the field’s adaptive responses to emerging global challenges.

3.3.2. The Timeline Visualization of References

By analyzing the supply chain risk research timeline diagram (Figure 7a), we can trace the evolution and development of various thematic groups over time, identifying emerging topics, classic topics, and those that have become relatively outdated. The timeline diagram, depicted in Figure 7a, organizes 15 groups vertically by size, providing a structured view of their significance within the field.
Classic topics maintain a perennial importance in the study of supply chain risk, regardless of their age. These include No. 3 “Random Yield”, No. 5 “Group Risk Management”, No. 12 “Reactive Scheduling”, and No. 1 “Uncertainty Handling”. Despite not being the latest areas of research, these topics continuously hold a critical position due to their foundational role and extensive interconnections with other subjects in the field.
On the other hand, relatively outdated topics show fewer connections with other groups and lack prominence on their respective timelines. Topics such as No. 2 “COVID-19”, No. 9 “Dual Channel”, and No. 14 “Sustainable Supply Chain Management” are examples where subsequent development has been limited, indicating a shift away from these areas toward more current issues.
Emerging topics, highlighted by their ongoing activity on the timeline, are poised to become future research hotspots. These include No. 0 “Robust Optimization”, No. 1 “Supply Chain Resilience”, No. 4 “COVID-19 Pandemic”, No. 6 “Ripple Effect”, No. 7 “Risk Assessment”, No. 8 “Blockchain”, No. 10 “Supply Chain Finance”, and No. 11 “Industry 4.0.” The continued focus on these areas suggests a dynamic shift in research priorities, with significant implications for future innovations and strategic directions in supply chain risk management.
This comprehensive analysis of the timeline not only helps us understand the historical progression of topics but also guides the identification of future research directions by illuminating the shifting focus of academic and industry interest within the field of supply chain risk.
The article published by Fahimnia and Jabbarzadeh [10] in 2016 belongs to Group 0 and has been cited 120 times. The article emphasizes the importance of supply chain disruption resistance and sustainability and proposes a hybrid approach to designing supply network sustainability to cope with random disruptions. By developing a stochastic bi-objective optimization model and fuzzy c-means clustering, the article quantifies and evaluates supplier sustainability. The model identifies outsourcing decisions and resilience strategies that minimize expected total costs and maximize sustainable performance under disruption. In a case study of the plastic pipe industry, the model demonstrates important management implications and practical implications.
We further calculated the citation distribution of these eight articles in recent years (Figure 7b), and we can predict that these articles may be mentioned again in the next few years.

4. Summary and Future Outlook

4.1. Cross-Integration of Multiple Disciplines in Supply Chain Risk Management

The examination of the scientometric landscape from 2004 to 2023 illustrates that supply chain risk management (SCRM) remains a vibrant area of scholarly interest, characterized by a substantial increase in publications, robust scientific collaborations, and dense citation networks. An analysis of the structural and temporal characteristics of relevant publications reveals that while the prominent topics within the field have evolved over time, certain contemporary keywords have emerged as potential focal points for future research. These include robust optimization, supply chain resilience, blockchain, supply chain finance, and Industry 4.0, all of which are at the core of the most recently cited papers exhibiting significant citation bursts.
These insights are corroborated by visual data from keyword cluster plots and reference timeline plots, which highlight both the persistence of classic topics and the emergence of new research areas. The involvement of multiple disciplines is increasingly evident, as demonstrated by the shift in subject categories and the diverse range of topics that now contribute to SCRM. The results of the keyword timeline visualization emphasize that while risk management, dynamic scheduling, and uncertainty handling remain foundational topics, new areas such as container transportation, adaptability, partial least squares, and the adoption of blockchain applications are gaining prominence as emerging research themes.
This interdisciplinary integration suggests a trend toward more complex and holistic approaches in tackling supply chain risks, reflecting broader technological and organizational shifts. As the field continues to evolve, these intersections are likely to deepen, offering new perspectives and solutions to enhance the resilience and efficiency of supply chains in a rapidly changing global landscape.

4.2. Exploration of Emerging Themes

Robust Optimization. Robust optimization is a strategic approach to uncertainty in decision-making, transforming complex problems into solvable convex optimization challenges of polynomial complexity through robust equivalent modeling [11]. This method is crucial in supply chain management, where uncertainties stem from internal operations or external emergencies, such as natural disasters. By applying robust optimization, companies can achieve optimal decisions in uncertain environments, ensuring the stability and sustainability of the supply chain. Researchers have effectively used these strategies to develop emergency network models and manage networks with demand uncertainty [11,12,13,14,15], thus enhancing the resilience of supply chain management against unpredictable disturbances.
Supply Chain Resilience. Defined as the ability of a supply chain to return to its original or an improved state post-disruption, supply chain resilience was first highlighted by Christopher and Peck in 2004 [16,17]. The importance of this concept has been underscored by the COVID-19 pandemic, driving extensive research into assessing and improving the resilience of supply chain networks [5,18,19,20,21,22,23,24,25,26,27,28,29]. Companies are encouraged to develop flexible production layouts and strengthen collaborations to adapt to market changes and customer demands, thereby enhancing competitive advantages [27,30,31,32,33,34,35].
Blockchain. As a secure, tamper-proof decentralized ledger, blockchain technology allows for companies to record transactions, logistics, and product information with high transparency [36]. This capability not only ensures accurate risk assessment but also facilitates the execution of smart contracts, streamlining processes and reducing intermediary costs [37]. Blockchain’s application in supply chain management contributes significantly to enhancing operational stability, efficiency, and sustainability [38,39,40].
Supply Chain Finance. This financial service model enhances the overall efficiency and liquidity of supply chains, supporting particularly small-to-medium enterprises. It integrates closely with real industrial economic activities, optimizing financial flows to reduce risks and improve supply chain resilience [41,42,43]. Real-time monitoring and risk management measures become feasible through advanced supply chain finance platforms, fostering cooperation and trust within the supply chain.
Industry 4.0. Characterized by smart manufacturing, Industry 4.0 leverages the Internet of Things, big data, and cloud computing to transform industrial processes [44,45]. This revolution promotes intelligence, automation, and networking in production, addressing traditional supply chain challenges, such as cumbersome processes and information asymmetry [46,47]. Digital transformation through Industry 4.0 enhances the flexibility and response capability of supply chains, significantly reducing operational costs and uncovering potential risks through advanced data analysis.
These emerging themes are pivotal in advancing supply chain stability and security. Each theme contributes uniquely—from optimizing decision-making under uncertainty with robust optimization, enhancing transparency with blockchain, and providing financial tools and support through supply chain finance to transforming industrial processes in Industry 4.0. Moving forward, the focus in supply chain risk research will likely continue to enhance resilience, with potential significant advancements in these key areas.
Looking forward to the future, supply chain risk research will continue to focus on how to improve the resilience of the supply chain. Enterprises need to enhance the adaptability and stress resistance of the supply chain in the face of various emergencies and long-term challenges through technological innovation and strategic optimization. With the continuous development and breakthrough of robust optimization, blockchain technology, and supply chain finance, supply chain management will gradually move towards a new era of more efficiency, more safety, and more transparency. Enterprises will be able to improve their competitiveness and achieve sustainable development by relying on these innovative tools and methods in the complex global economic environment.
In short, future supply chain management is not only a process of meeting the challenges of risks and uncertainties but also the process of seizing opportunities and using emerging technologies and financial instruments to promote the progress of the industry. By continuously exploring and applying these cutting-edge topics, enterprises will be able to better meet future challenges, maintain the resilience and stability of the supply chain, and promote the innovation and development of the whole industry.

4.3. Further Directions

Sustainable Supply Chain Risk Management. This direction integrates sustainability at the heart of supply chain risk management practices, tackling environmental, social, economic, and technological risks. This approach emphasizes the inclusion of sustainability factors in risk assessments and decision-making processes to boost overall resilience. According to Kamalahmadi and Parast [48], researchers are actively investigating ways to incorporate environmental, social, and economic sustainability into supply chain operations. The goal is to effectively address a range of sustainability challenges while simultaneously enhancing the resilience of the supply chain [10,22,49,50,51,52,53,54,55,56,57,58,59,60,61].
Digital Supply Chain Risk Management. This area focuses on leveraging advanced information technologies—such as big data analytics, the Internet of Things (IoT), and blockchain—to monitor, evaluate, and manage risks comprehensively within the supply chain. The goal is to enhance the elasticity and resilience of supply chains and minimize the impact of risks on business operations [62,63,64,65]. With a core reliance on data analysis, businesses can continuously monitor the status and changes within the supply chain, predict and identify potential risks, and ensure data authenticity and integrity through technologies such as the IoT and blockchain [45,47,50,66,67,68,69].
Multi-level Supply Chain Risk Management. Given the complexity and multi-layered structure of supply chains, this research theme focuses on effective risk management across various levels and among different participants. It includes developing cross-departmental and cross-organizational risk information sharing and cooperation mechanisms to enhance communication and coordination, thus improving the risk management capabilities of the entire supply chain system [9,70,71,72,73]. Researchers advocate for establishing clear indicators and methods for risk identification, assessment, and monitoring, along with risk warning and response mechanisms. This ensures that all levels of the supply chain can detect and respond to potential risks promptly. Moreover, considering the challenges of information asymmetry and cooperation, there is a push for establishing platforms for information sharing and cooperation to facilitate the efficient circulation and sharing of information, enhancing the supply chain’s flexibility and capacity to recover and adapt swiftly in the face of risks.

Author Contributions

Conceptualization, Z.H.; methodology, T.D.; software, T.D.; validation, T.D.; formal analysis, T.D.; resources, Z.H.; writing—original draft, T.D.; writing—review & editing, Z.H.; supervision, Z.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. The number of publications by year and journal. (Note: In (a) the red line represents the yearly publication number and the black line represents the deming linear fitting curve; (b) The blue bar represents the number of journal publications).
Figure 1. The number of publications by year and journal. (Note: In (a) the red line represents the yearly publication number and the black line represents the deming linear fitting curve; (b) The blue bar represents the number of journal publications).
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Figure 2. Publication citation chart (color bar from right (white) to left (red) represents 2004 to 2023). (Note: Nodes represent different co-cited literature, larger nodes represent higher number of co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborations, and the purple color in the outer circle represents the high centrality).
Figure 2. Publication citation chart (color bar from right (white) to left (red) represents 2004 to 2023). (Note: Nodes represent different co-cited literature, larger nodes represent higher number of co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborations, and the purple color in the outer circle represents the high centrality).
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Figure 3. (a) National cooperation network. (b) Institutional cooperation network. (c) Author cooperation network. (Note: In subfigures (a,b) nodes represent different countries/institutions, larger nodes represent higher number of publications, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborative relationships, and the purple color in the outer circle represents high centrality; In (c) different nodes represent different authors, the size of the dots represents the number of publications, the line connecting the dots represents the collaboration, the width of the line represents the intensity of the collaboration, and different colors represent different clusters).
Figure 3. (a) National cooperation network. (b) Institutional cooperation network. (c) Author cooperation network. (Note: In subfigures (a,b) nodes represent different countries/institutions, larger nodes represent higher number of publications, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborative relationships, and the purple color in the outer circle represents high centrality; In (c) different nodes represent different authors, the size of the dots represents the number of publications, the line connecting the dots represents the collaboration, the width of the line represents the intensity of the collaboration, and different colors represent different clusters).
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Figure 4. Top 50 most cited subject categories. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the subject category).
Figure 4. Top 50 most cited subject categories. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the subject category).
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Figure 5. Top 50 keyword emergence. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the keyword bursts).
Figure 5. Top 50 keyword emergence. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the keyword bursts).
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Figure 6. Keyword clustering. (Note: from (a–d) represent the time period from 2004–2023, different colors represent different clusters, and colors from red to pink represent clusters from 0 to 7).
Figure 6. Keyword clustering. (Note: from (a–d) represent the time period from 2004–2023, different colors represent different clusters, and colors from red to pink represent clusters from 0 to 7).
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Figure 7. (a) The timeline chart of cited documents. (b) The citation dynamics of these 8 publications. (Note: (a) Nodes represent different co-cited literature, larger nodes represent more co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent the co-citation relationship, the purple color in the outer circle represents high centrality, and the labels on the right hand side of the graph represent different clusters; (b) The different lines represent the most co-cited documents in clusters 0, 1, 4, 6, 7, 8, 10, and 11, respectively, and the change in the line represents the change in the co-citation count per year since the publication of the document).
Figure 7. (a) The timeline chart of cited documents. (b) The citation dynamics of these 8 publications. (Note: (a) Nodes represent different co-cited literature, larger nodes represent more co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent the co-citation relationship, the purple color in the outer circle represents high centrality, and the labels on the right hand side of the graph represent different clusters; (b) The different lines represent the most co-cited documents in clusters 0, 1, 4, 6, 7, 8, 10, and 11, respectively, and the change in the line represents the change in the co-citation count per year since the publication of the document).
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Table 1. Supply chain risk literature retrieved through WOS.
Table 1. Supply chain risk literature retrieved through WOS.
CategoriesPublicationArticlesReviewAuthorsInstitutionsJournalsSubject Categories
Amount12,67711,76491328,88075682210201
Table 2. The references with citation bursts at different periods.
Table 2. The references with citation bursts at different periods.
ReferencesYearStrengthBeginEnd2004–2023
Tomlin B, 2006, MANAGE SCI,200637.8520072011Sustainability 16 05261 i001
Tang CS, 2006, INT J PROD ECON200642.0520082011Sustainability 16 05261 i002
Craighead CW, 2007, DECISION SCIENCES200733.4220082012Sustainability 16 05261 i003
Manuj I, 2008, J BUS LOGIST200828.620092013Sustainability 16 05261 i004
Tang O, 2011, INT J PROD ECON201146.8220122016Sustainability 16 05261 i005
Bode C, 2011, ACAD MANAGE J201129.4620132016Sustainability 16 05261 i006
Sodhi MS, 2012, PROD OPER MANAG201248.0820142017Sustainability 16 05261 i007
Baghalian A, 2013, EUR J OPER RES201334.2520142018Sustainability 16 05261 i008
Sawik T, 2013, OMEGA-INT J MANAGE S201330.3120142018Sustainability 16 05261 i009
Heckmann I, 2015, OMEGA-INT J MANAGE S201585.5520152020Sustainability 16 05261 i010
Brandon-Jones E, 2014, J SUPPLY CHAIN MANAG201436.320152019Sustainability 16 05261 i011
Wieland A, 2013, INT J PHYS DISTR LOG201333.6820152018Sustainability 16 05261 i012
Chopra S, 2014, MIT SLOAN MANAGE REV201433.6220152019Sustainability 16 05261 i013
Pettit TJ, 2013, J BUS LOGIST201329.6120152018Sustainability 16 05261 i014
Ambulkar S, 2015, J OPER MANAG,201562.1420162020Sustainability 16 05261 i015
Hohenstein NO, 2015, INT J PHYS DISTR LOG201548.3420162020Sustainability 16 05261 i016
Tukamuhabwa BR, 2015, INT J PROD RES201546.9120162020Sustainability 16 05261 i017
Fahimnia B, 2015, EUR J OPER RES201542.6420162020Sustainability 16 05261 i018
Kim Y, 2015, J OPER MANAG201535.9920162020Sustainability 16 05261 i019
Torabi SA, 2015, TRANSPORT RES E-LOG201531.7220162020Sustainability 16 05261 i020
Ivanov D, 2014, INT J PROD RES201431.1420162019Sustainability 16 05261 i021
Chiu CH, 2016, ANN OPER RES20163120162020Sustainability 16 05261 i022
Ho W, 2015, INT J PROD RES201593.8820172020Sustainability 16 05261 i023
Kamalahmadi M, 2016, INT J PROD ECON,201657.5820172021Sustainability 16 05261 i024
Snyder LV, 2016, IIE TRANS201657.0520172021Sustainability 16 05261 i025
Scholten K, 2015, SUPPLY CHAIN MANAGE201541.4620172020Sustainability 16 05261 i026
Giannakis Mihalis, 2016, INTER …… ODUCTION ECONOMICS201640.6620172021Sustainability 16 05261 i027
Hasani A, 2016, TRANSPORT RES E-LOG201628.6520172021Sustainability 16 05261 i028
Ivanov D, 2017, INT J PROD RES201734.7420182021Sustainability 16 05261 i029
Chowdhury MMH, 2017, INT J PROD ECON201729.0420192023Sustainability 16 05261 i030
Table 3. The references with citation bursts from the beginning to 2023.
Table 3. The references with citation bursts from the beginning to 2023.
BeginEndStrengthYearTypeTitle
2019202329.042017ArticleSupply chain resilience: Conceptualization and scale development using dynamic capability theory
2019202325.112017ArticleSupply chain capabilities, risks, and resilience
20192023252017ReviewAnalyzing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review
2020202315.662017ArticleSupply chain resilience in a developing country context: a case study on the interconnectedness of threats, strategies and outcomes
2021202314.852020ArticleViability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak
2021202314.462020ArticleThe Food Systems in the Era of the Coronavirus (COVID-19) Pandemic Crisis
2020202313.762017ReviewResilience in Business and Management Research: A Review of Influential Publications and a Research Agenda
2021202313.62020ArticlePredicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case
2021202313.492020ArticleFood supply chains during the COVID-19 pandemic
2018202313.242017ArticleResilient supply chain network design under competition: A case study
2021202312.992020ArticleA decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)
2020202312.842017ArticleToward an integrated sustainable-resilient supply chain: A pharmaceutical case study
2018202312.682017ArticleSupply chain resilience: model development and empirical analysis
2020202312.142017ArticleAn information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences
2018202311.392017ArticleThe impact of risk management on the frequency of supply chain disruptions A configurational approach
2021202311.282017ArticleBuilding resilience in SMEs of perishable product supply chains: enablers, barriers and risks
2021202311.282017ArticleTechnological capabilities and supply chain resilience of firms: A relational analysis using Total Interpretive Structural Modeling (TISM)
2020202310.252017ArticleMitigating disruptions in a multi-echelon supply chain using adaptive ordering
2020202310.252019ArticleA Trade Credit Model with Asymmetric Competing Retailers
2020202310.252019ArticleSupply chain sustainability risk and assessment
2019202329.042017ArticleSupply chain resilience: Conceptualization and scale development using dynamic capability theory
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Ding, T.; Huang, Z. Uncovering the Research Hotspots in Supply Chain Risk Management from 2004 to 2023: A Bibliometric Analysis. Sustainability 2024, 16, 5261. https://doi.org/10.3390/su16125261

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Ding T, Huang Z. Uncovering the Research Hotspots in Supply Chain Risk Management from 2004 to 2023: A Bibliometric Analysis. Sustainability. 2024; 16(12):5261. https://doi.org/10.3390/su16125261

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Ding, Tianyi, and Zongsheng Huang. 2024. "Uncovering the Research Hotspots in Supply Chain Risk Management from 2004 to 2023: A Bibliometric Analysis" Sustainability 16, no. 12: 5261. https://doi.org/10.3390/su16125261

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