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12 July 2024

Simulation Methods and Digital Strategies for Supply Chains Facing Disruptions: Insights from a Systematic Literature Review

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1
BTS CSCO Advisory, Cloud Success Services, SAP Deutschland SE & Co. KG, Hasso-Plattner-Ring 7, 69190 Walldorf, Germany
2
Business Organization Department, ROGLE, Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
3
Department of Industrial Engineering, University of Applied Sciences Ansbach, Residenzstraße 8, 91522 Ansbach, Germany
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Author to whom correspondence should be addressed.
Sustainability2024, 16(14), 5957;https://doi.org/10.3390/su16145957
Version Notes

Abstract

Supply chain disruptions pose significant economic stability and growth challenges, impacting industries globally. This study aims to systematically review the literature on the use of simulation tools in managing supply chain disruptions, focusing on the historical evolution, prevalent simulation methods, specific challenges addressed, and research gaps. A systematic literature review was conducted using the PRISMA method. An initial pool of 236 articles was identified, from which 213 publications were rigorously reviewed. This study analyzed these articles to map the academic landscape, identify key clusters, and explore the integration of digital advancements in enhancing supply chain resilience. The review identified the chronological development of research in this field, highlighting significant contributions and influential authors. It was found that various simulation methods, including discrete-event simulation, agent-based modeling, and system dynamics, are employed to address different aspects of supply chain disruptions. Two primary research frontiers emerged from the analysis: the strategic reconfiguration of supply chain networks to mitigate ripple effects and the swift implementation of countermeasures to contain disruptions. The findings suggest a need for future research focusing on dynamic analysis and control theory applications to understand and manage supply chain disruptions better. This study also notes the increasing interest and need to use digital technologies (digital twins, artificial intelligence, etc.) in future research. It underscores the necessity for continued research to develop resilient and sustainable supply chain infrastructures aligned with the United Nations’ Sustainable Development Goals. The identified research gaps offer a roadmap for future scholarly exploration and practical implementation.

1. Introduction

To cite the United Nations’ 17 Sustainable Development Goals (SDGs) to transform our world: “[They] are a call for action by all countries–poor, rich and middle-income–to promote prosperity while protecting the planet. They recognize that ending poverty must go hand-in-hand with strategies that build economic growth and address social needs, including education, health, social protection, and job opportunities, while tackling climate change and environmental protection” [1]. Three SGDs that benefit from the deeper investigation of supply chain disruptions using simulation techniques are goals 8, 9, and 12. Goal 8 postulates that multiple crises, such as COVID-19 and several wars, place the global economy under serious threat. The effects of this crisis have also hit supply chains in recent years. Inventing strategies to better cope with the impact of such crises and build more resilient supply chains has already been postulated by authors such as Y. Wang [2] and D. Ivanov in collaboration with A. Dolgui [3]. Given that they are encouraging more intense research in this area, they are directly meeting the requirements for supporting the achievement of goal number 8 of the United Nations’ SDG catalog.
In SDG 9.1, the United Nations (UN) emphasizes the development of reliable, sustainable, and resilient infrastructure, including regional and transborder infrastructure, to support economic growth and human well-being, focusing on affordable and equitable access for all [4]. The continuous evolution of management paradigms, supported by systematic network frameworks and simulation methods, will be key to building resilient and sustainable infrastructure [3]. Closed-looped supply chains (CLSCs), as required in UN SDG 12.5, should help to reduce waste generation by 2030. This aligns with Katsoras’ and Georgiadis’ paper [5,6], in which a system dynamics (SD) model for a single manufacturer/multi-echelon CLSC was created. These findings highlight the need for different mitigation policies based on the economic and inventory focus.
This paper aims to understand better the ripple effect in supply chains (SCs) and the influence of network structures on coping with this effect. It builds on the concepts of Dolgui and Ivanov, which state a need for future research in the dynamic analysis of the SC ripple effect (simulations and control theory) and its influence on network structures [3,7]. This is all conducted to support the SDGs through a better understanding of disruptions in SCs through a literature review. Based on these ideas, the following research questions (RQ) will be investigated:
  • RQ1: What is the chronological evolution of this scientific field?
  • RQ2: Which simulation methods are most commonly employed in this research domain?
  • RQ3: Which simulation methods are utilized for specific supply chain challenges?
  • RQ4: What are the existing research gaps and promising research directions?
These questions aim to guide researchers in reviewing the current situation in the propagation of disruptions in supply networks and understanding how this challenge is approached in the literature. Research question 1 (RQ1) aims to recognize how scientists have analyzed novel situations and included historical events to cover new research gaps and enrich the scientific literature. Likewise, recent advances in technologies and simulation tools are relevant factors that have led to novel proposals in the literature. Identifying whether domain clusters exist, and which types of simulation methods are preferentially and historically used, can serve as a guideline for future contributions (RQ2). Furthermore, the appearance of new types of disruptions (pandemic, war, etc.) poses new challenges to existing SCs. Determining whether specific simulation methods are used in such contexts would be an interesting point for future research (RQ3). Lastly, with such an extensive quantity of papers published over the last decade, categorizing the existing research gaps to highlight promising research directions would help the scientific community to identify potential novelties (RQ4).
This paper is structured as follows. Section 2 addresses previous literature reviews, describes the applied methodology in detail regarding the applied research protocol and information analysis, and introduces the PRISMA flow diagram. Section 3 includes a descriptive analysis of the results, including relevant information such as annual publications, relevant authors, and most-cited papers. Section 4 proposes clustering the existing literature, discussing the main research streams, gaps, and opportunities for each cluster, and finally analyzing the usage of simulation methods. Lastly, Section 5 presents the conclusions derived from the work carried out.

2. Systematic Literature Review

2.1. Previous Literature Reviews

The scientific literature contains several literature reviews of SC disruptions. Nevertheless, existing reviews that include studies on simulation techniques are scarce. Table 1 illustrates the previously conducted literature reviews with a research focus on using simulation methods to analyze the impact on SCs after a network disruption. The focus is split into two main areas within the research field. The first is supply chain management (SCM), the classic definition of handling the downstream logistics chain. The second is manufacturing, focusing on the operative manufacturing of one participant in a SC.
Dy et al. [8] reviewed 82 publications and analyzed the applicability of digital twin (DT) technologies to cope with risk disruptions. The objective of this paper was to explore the integration of the metaverse and Quality 4.0 to enhance manufacturing system resilience during crises, with a focus on the COVID-19 pandemic. Through a comprehensive literature review, it offers insights into supporting technologies and applications, aiming to provide valuable guidance for greater resilience in global manufacturing processes. The paper explores DT applications in the energy sector, categorizing them into low-carbon city and smart grid levels while addressing interoperability and data processing challenges. It suggests solutions like knowledge graph analysis and emphasizes the role of AI in advancing DT applications for improved energy system resilience. Since DT technologies are not congruent with simulation techniques, this review can be delimited from the review executed for this paper.
Soto and Aguila [9] focus on manufacturing, reviewing 158 publications. Operational and disruption risks in manufacturing paradigms like Flexible Manufacturing Systems (FMSs), Reconfigurable Manufacturing Systems (RMSs), and Smart Manufacturing Systems (SMSs) are investigated. Distinctive risk strategies are identified, with FMSs emphasizing reactive approaches, RMSs prioritizing adaptability, and SMSs focusing on proactive measures, categorizing risks into facets such as investment, safety, and cybersecurity. The paper extensively maps risks and strategies for different manufacturing paradigms. It proposes diverse research directions for future exploration, including redesigning layouts, retrofitting paths, incorporating AI, studying DT, and involving SMEs.
El Jaouhari et al. [10] explore the metaverse and Quality 4.0 intersection to boost manufacturing system resilience, particularly during crises like the COVID-19 pandemic, utilizing a systematic literature review (SLR) from 2012 to 2023. It addresses key research questions about technologies, antecedents, stages, and pandemic-specific applications of metaverse-enabled Quality 4.0, aiming to provide insights for enhancing the resilience of global manufacturing processes. The main findings were a failure to use artificial intelligence and a sufficient exploration of integrating the metaverse (MV), citing approaches to MV-based Quality 4.0 and manufacturing resilience.
As Refs. [9,10] focus mainly on manufacturing execution, they are not comparative publications.
Llaguno et al. [11] provide an overview of nine disruption risk types in SCs, including natural disasters and delays, and discuss mitigation measures like backup suppliers and excess inventory. This paper explores conceptual frameworks for operational risk management, emphasizing the ripple effect in general SCs and delving into specific cases, with particular attention paid to the impact of digitalization in the Industry 4.0 context. It aims to understand disruption risks and mitigation strategies in SCM comprehensively.
Asan and Usta [12] address the research gap on risks in Service Supply Chains (SSCs) by systematically reviewing the existing literature. They identify and define associated risks, explore their interactions, and utilize Interpretive Structural Modeling (ISM) and Matrix-based Multiplication Applied to a Classification (MICMAC) for hierarchical representation. Validated through a real-world case study, this study provides clear definitions of SSC risk categories and a structural model illustrating the nature and consequences of these risks, ultimately aiming to enhance the understanding, identification, and management of risks in SSCs for improved overall performance.
Vieira et al. [13] emphasize simulations as a data integration tool, enabling the analysis to be conducted using data from multiple relevant sources, thereby improving the quality of such analysis. This paper explores digital twins to connect theoretical concepts with practical implementations. It analyzes historical DT efforts, assesses the impact of allied technologies like machine learning, and reviews domain influences on DT implementation. This paper evaluates current limitations, proposes a conceptualization for DT components, and explores its diverse facets to provide comprehensive insights into potential applications and advancements.
Table 1 shows an overview of the executed literature reviews and provides an overview of chronological data, the number of analyzed publications, the main findings, and the key objectives.
After comparing the objectives and key findings of the relevant publications summarized in the section above, it becomes clear that a new review is needed. It can be stated that no existing review covers the specific evolutionary steps in this research or how they are interconnected. Furthermore, it has not been clearly stated which simulation methods are used for which kind of simulative challenges or if the authors determined the technique based on their preferences. Additionally, it is unclear who the most influential authors in this research area are.
Table 1. Previous literature reviews in the area of supply chain disruptions in combination with simulation techniques (SCM: supply chain management; Mnfg: manufacturing; SSC: service supply chain LR: literature review; SLR: systematic literature review; Dis: dissertation).

2.2. Literature Review Methodology

This paper proposes a systematic literature review (SLR) that follows [14], where approximately 240 publications have been carefully selected, analyzed, and classified. Starting with the scientific contribution made by answering the research questions, the relation to the literature protocol is provided in this section.
Concerning Research Question 1 (RQ1), the historical development of the research topic is thoroughly examined, encompassing the identification of the most relevant journals, influential authors, and pertinent publications.
To address Research Question 2 (RQ2), the employed simulation methods have been scrutinized across the entire spectrum of publications. In addition, the historical evolution of the utilized simulation methods has been elucidated, and redeployment effects have been considered.
In response to Research Question 3 (RQ3), a more in-depth examination has been conducted based on the evaluation carried out for RQ2. The entire collection of publications utilizing simulation techniques has been analyzed thoroughly. The specific simulation technique used by each author has been identified. Subsequently, the prior publications of each author have been scrutinized for instances of employing this simulation technique.
To answer RQ4, scientific maps have been used to highlight how the current research trends are delineated while identifying potential future research directions and open research areas.
The research protocol introduced a definition for the inclusion and exclusion criteria (Table 2). These criteria were significantly influenced by Ivanov’s assertion that the ripple effect and related research constitute a relatively novel and underexplored area of inquiry [3]. Consequently, the decision was not to impose a chronological limitation within the research strategy. As the transition from the literature protocol to the SLR unfolded, this initial boundary condition was duly validated.
Table 2. Inclusion and exclusion criteria of the search strategy.
Furthermore, publications authored in English and German, identified as the most influential languages through the conducted SLR, were deemed relevant. The preliminary assumption guiding the limitation criteria also entailed excluding the research area of computer science and papers with a direct technological focus. This exclusion was deemed necessary due to the notable number of publications focused on software maintenance and development approaches that persisted within the query results.
Throughout the successive phases of the PRISMA procedure, the execution and inclusion criteria were continually refined. Notably, it became evident that as the research query evolved into its final form, the exclusion above criteria lost their applicability. As such, the definitive components of the search strategy are delineated in the table below.
Inclusion and exclusion criteria establish the framework to tailor the search strategy to the predefined objectives of an upcoming literature review. Given developments in the inclusion and exclusion criteria, along with a slight adjustment to the research question to encompass simulation techniques, the search queries must also be updated during the recent research activities. The following passage describes the progression from the initial search query used in the literature review protocol to the final query employed in the SLR. The initial search term, stipulated in the review protocol for WoS and Scopus, is provided below (Table 3). It was divided into three primary search blocks, interconnected through logical ‘AND’ operators. The first block restricts the search results to the subject area of SCM. The intermediate search block refines the results to SC networks as a specific topology within SCs. Lastly, relevant synonyms for ‘disruption’ are included to narrow the search outcomes further. Following the research protocol and the exclusion and inclusion criteria at that time, the research field of ‘computer science’ was excluded. The search yielded 82 pertinent records in WoS and 141 relevant records in Scopus.
Table 3. Initial research queries used in the SLR.
The in-depth exploration of the research topic facilitated the identification of potential open research areas. Furthermore, the research activities highlighted an underrepresentation of simulation techniques in the search query outlined in the protocol. These factors led to significant adaptations in the search terms utilized for the SLR, as delineated in the table below. Upon comparing the search phrases of the protocol and the SLR, it becomes evident that the overarching structure consisting of three blocks has been retained, albeit with underlying structural adjustments. The block that enumerates synonyms for ‘supply chain disruptions’ has been retained and now occupies the foremost position within the sequence of the search blocks. Conversely, the block elaborating on SC networks as distinct network topologies has been eliminated, making way for the term ‘supply chain’. Throughout the research journey, it became apparent that narrowing the focus to a specific SC topology, such as SC networks, no longer aligned with the updated research question. The scope of the research was broadened, from disruptions exclusively within SC networks to disruptions in the broader context of SCM. Moreover, it was observed that the condition ‘supply chain management’ AND ‘supply chain resilience’ was inadvertently limiting the search results through a filter that no longer accurately mirrored the updated research focus. As a result, the blocks (‘supply chain network’ OR ‘supply network’) and (‘supply chain management’ OR ‘supply chain resilience’) were consolidated into a single entity, ‘supply chain management’. Another aspect that had not been adequately addressed in the initial search query was the incorporation of simulation techniques. To rectify this, the primary simulation techniques (‘agent-based modeling’, ‘digital twin’, ‘discrete event simulation’, ‘system dynamics’) were incorporated into the third search block (Table 4).
Table 4. Final research queries used in the SLR.

2.3. Screening and Selection Phase

The results obtained from the database queries established the scope for the bibliometric analysis of the research area. Utilizing these results, the screening phase of the PRISMA procedure commenced (Supplementary File S1). All chosen publication titles, keywords, and abstracts were meticulously categorized. This classification was based on the criteria predefined in the literature review protocol outlined below (Table 5).
Table 5. Classification of screened articles.
Throughout the execution of the screening phase, following the application of the exclusion and inclusion criteria, a total of 15 publications were excluded. The ensuing eligibility phase, marked by the amalgamation of results from both query statements, yielded a tally of 148 publications classified as pertinent. In addition, 29 publications were omitted due to the unavailability of the full-text version, where access was requested but not obtained by the authors. Ultimately, 213 publications were identified for comprehensive full-text screening. A visual representation of the streamlined outcomes of the PRISMA procedure is presented in Figure 1.
Figure 1. PRISMA flow diagram results.
In the screening process, 236 articles retrieved from WoS were identified as relevant. In addition, 21 articles from Scopus were added. The analyzed sample in the subsequent section comprises 213 publications authored by 568 authors affiliated with 135 institutions and published in 138 journals. These articles have been cited 4960 times (as shown in Table 6 below).
Table 6. Descriptive statistics of the used dataset.

3. Descriptive Analysis of the Information

3.1. Annual Scientific Production

The chronological distribution of publications within the dataset is depicted in Figure 2. The earliest article in the selected context dates to 2006, marking the inception of the trend. From that point onward, there is a discernible upward trajectory. This chronological development serves as compelling evidence for the rising relevance of the research area.
Figure 2. Chronological distribution of publications.
This growth pattern can be segmented into three distinct phases: (1) between 2006 and 2014, a phase of relative stagnation at a modest level is evident; (2) from 2015 to 2018, the number of publications ascended to a higher plateau, reaching an approximate average of 10 publications annually; and (3) since 2019, a substantial surge in publications occurred, culminating in 42 publications in the previous year. Projecting the figures for the first two quarters of 2023, it is plausible that the publication count will mirror that of 2022.

3.2. Most Cited Papers and Journals

The following study evaluates the most-cited publications in the adopted research field, limited to articles cited at least 100 times (Table 7). The average citation rate of the sample is 55 citations. Notably, 19.89 percent of all publications have not yet had any citations. The most-cited article, “Predicting the Impacts of Epidemic Outbreaks on Global Supply Chains: A Simulation-Based Analysis on the Coronavirus Outbreak (COVID-19/SARS-CoV-2) Case,” has been cited 731 times. This article simulates the effects of epidemic outbreaks on SCs, using the example of the coronavirus outbreak [15,16,17].
Table 7. Most-cited publications in the research area.
The second most-cited article is “A Digital Supply Chain Twin for Managing Disruption Risks and Resilience in the Era of Industry 4.0” with 449 citations. This article proposes an approach for modeling a DT of a SC to enhance coping mechanisms for exogenous shocks and to extend SC visibility [3].
The third most-cited article, titled “‘A Blessing in Disguise’ or ‘As If It Was not Hard Enough Already’: Reciprocal and Aggravate Vulnerabilities in the Supply Chain” authored by D. Ivanov [18], investigates the interrelations of structural and operational vulnerabilities in the SC using DES.
A Pareto analysis (Figure 3) was conducted to find the most relevant journals in the research area. The “International Journal of Production Research (IJPR)” is the most relevant journal, having published 8.9 percent of all publications. The second-most relevant journal is the conference proceedings publication of the “Winter Simulation Conference (WSC)”, which has published 6.3 percent of all analyzed articles. The third-most relevant journal is “Computers & Industrial Engineering (CAIE)”, which has published 5.2 percent of all articles. These three journals have published over 20 percent of all articles. All relevant journals can be seen in the diagram below, which shows the number of publications arranged in a chronological cluster. The oldest cluster starts in 2009, the first year a publication in this dataset appeared, and ends in 2014. The middle cluster begins in 2015 and lasts through 2019, while the most recent cluster starts in 2020 and ends in 2023. The graph illustrates that the leading publications have appeared in the last three years, highlighting the relevance of this research journey. Looking at the bar chart of IJPR, it becomes clear that this topic increased relevance in this journal in the last three years because the green cluster is the biggest. Furthermore, it can be stated that this journal has gained interest in this research topic in recent history since no publications have appeared in the yellow cluster. The situation of the WSC and CAIE journals looks different: Publications in the yellow cluster can be seen to show how these journals showed early interest in this topic. The blue and green clusters (extrapolation through the end of the year) are increasing, suggesting that these journals have gained even more interest in this topic in recent years. This is even more striking given that these journals are top-ranked and renowned, underlining the relevance of this research topic. Additionally, the development of the journal “Computer-Aided Chemical Engineering” is worth mentioning. In the early beginnings of this journal, it concentrated on disruption analysis in chemicals SCs under simulation methods [25,26,27] but lost interest after 2017.
Figure 3. Most influential journals.

3.3. Most Cited Authors

The analysis reveals that 66.01 percent of the authors have published only one paper, while 10.34 percent have published three or more. Table 8 proposes an ordered list of authors who have published at least three articles. In addition, the table provides insights into the impact of the most active authors. According to the table below, A. Dolgui, with an average of 164.60 citations, emerges as the most prominent author, followed by D. Ivanov (140.44) and B. Sokolov (106.50). However, Ivanov stands out as the most productive and influential author in the research area when considering the number of publications.
Table 8. Most influential authors in the research area.
After identifying the most influential authors, a robust relationship was observed among the key figures in this research domain, particularly in joint publications. Consequently, a correlation analysis was conducted on the co-authorship patterns of all authors within our scope, totaling 569 individuals. The analysis involved identifying and tallying pairs of authors who have collaborated on publications. After excluding pairs with no or only one joint publication, the resulting list of pairs is presented in Table 9. Upon examining the highlighted data, it becomes evident that the triumvirate of D. Ivanov, A. Dolgui, and B. Sokolov significantly dominates this research area.
Table 9. Co-authorship mapping of the most relevant authors.
Upon comparing the outcomes of the co-authorship analysis with the overall publication figures (Table 9), it becomes evident that these three authors predominantly collaborate on their publications. For instance, A. Dolgui has authored five papers, all co-authored by D. Ivanov. Similarly, B. Sokolov co-published all his papers with D. Ivanov, with three also involving A. Dolgui.

4. Classification and Analysis of the Information Obtained from the Selected Publications

4.1. Chronological Clustering of the In-Scope Articles and Analysis of the Most-Cited Articles

By setting the minimum threshold for the number of citations of a referenced work at ten, the original number of articles within the sample was reduced to 46 relevant articles. This was conducted to focus on the most relevant publications. Furthermore, this is the standard setting used in VosViewer v1.6 co-citation analysis.
Building upon these highly cited articles, a co-citation analysis was conducted (Figure 4), identifying the key literature in our research stream. This was carried out to show the interrelations of the articles in scope. By analyzing the articles more deeply, we identified a chronological development of the research area and that the identified clusters build upon each other.
Figure 4. Scientific map of co-citations.
The subsequent graph illustrates the 46 relevant references, segmented into three primary clusters. Notably, a discernible temporal evolution is evident across these clusters.
  • Cluster 1 [in red]: The average publication date of this cluster aligns with the early 2000s, signifying its origins.
  • Cluster 2 [in green]: Subsequently, the second cluster’s average publication date centers around 2010, marking its emergence.
  • Cluster 3 [in blue]: Encompasses the most recent publications, commencing around 2019.
This evolution underscores the progression of the interconnected research fronts (see Figure 5). Furthermore, the clustering shows that Cluster 1, which can be seen as the ground floor of this research area, is very pronounced.
Figure 5. Chronological distribution of the publication within the clusters.
Cluster 1 comprises 18 publications, with prominent works authored by M. Wilson like “The Impact of Transportation Disruptions on Supply Chain Performance” [19], cited 44 times; C. Craighead et al. article titled “The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities” [28], cited 26 times; and Y. Sheffi’s et al. “A Supply Chain View of the Resilient Enterprise” [29], with 21 citations.
Cluster 2 comprises 23 publications and is particularly noteworthy due to D. Ivanov’s contribution, as he accounts for 10 out of the 23 articles. This prolific output establishes him as one of the authors with the highest publication rates. Notably, the article with the most citations within this cluster is “Ripple Effect in the Supply Chain: An Analysis and Recent Literature” [30], published by A. Dolgui et al. in 2018. This work is followed by Ivanov and Schmidt’s publications, including “The Ripple Effect in Supply Chains: Trade-off ‘Efficiency-Flexibility-Resilience’ in Disruption Management” [31] and “A Quantitative Analysis of Disruption Risk in a Multi-Echelon Supply Chain” [32].
Cluster 3 encompasses six articles authored by seven distinct authors. Notably, all the most-cited articles within this cluster were penned by D. Ivanov. His notable contributions include “Predicting the Impacts of Epidemic Outbreaks on Global Supply Chains: A Simulation-Based Analysis on the Coronavirus Outbreak (COVID-19/SARS-CoV-2) Case” [15], published in 2021. The second and third most-cited publications, “Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives-Lessons from and Thinking Beyond the COVID-19 Pandemic” [33] and “Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak” [34] were published one year prior.

4.2. Definition of Key Clusters

4.2.1. Cluster 1: Foundations of SC Disruption and SC Resilience

Cluster 1 corresponds to the foundational literature in SC disruption and resilience. In 2007, Wilson delineated and simulated the repercussions of transportation disruption on SCs [19]. As one of the pioneers, Craighead formulated the characteristics of SC design and mitigation capabilities, providing invaluable insights for management decisions and mitigating the financial consequences of exogenous shocks on SCs [28]. In her work “An Empirically Derived Agenda of Critical Research Issues for Managing Supply Chain Disruptions”, J. Blackhurst delves into pertinent research topics that effectively address SC disruptions [35]. Another noteworthy contribution in Cluster 1 is P. Kleindorfer’s publication, which outlines an approach to managing disruptions in SCs [36]. The analysis conducted on articles within Cluster 1 underscores its significance as the foundational literature that marked the initial stages of research on SC disruptions and risk management in conjunction with simulation techniques.

4.2.2. Cluster 2: Ripple Effect in SC, Optimization and Simulation Approaches

In Cluster 2, depicted in green, the research endeavors concerning management approaches for addressing disruptions in SCs have been augmented with quantitative and simulation methodologies to enhance the visualization and characterization of disruptions. Furthermore, the term ‘supply chain resilience’ was introduced in the discourse on strategies to contend with exogenous shocks in SCs. One of the pioneers in this domain, Hosseini, reviewed quantitative methods in the context of SC resilience [37]. Alongside the well-established bullwhip effect in SCs, Ivanov introduced the ripple effect in 2014, elucidating the cascading impact of disruption propagation on SC performance and the consequential alterations in SC structural design and planning parameters due to disruptions [31]. Extending the exploration of the ripple effect, Ivanov, Sokolov, Dolgui, and others further enriched the research landscape by incorporating simulation methods like SD and DES, thus reinforcing their investigative endeavors [31,38]. In summation, the articles within Cluster 2 can be regarded as an evolution of the research initiatives established in Cluster 1. A new scientific domain has emerged by introducing novel research trajectories through the exploration of the ripple effect and synergizing advanced SCM approaches with intensified utilization of simulation techniques.

4.2.3. Cluster 3: Technologies 4.0 and SC Survivability in Extreme Disruptions

Cluster 3, denoted in blue and characterized by the most recent average publication years, signifies a progressive evolution within this research area. Cluster 3 can be delineated into three primary research pathways. The first path commences with publications from 2020, when the COVID-19 pandemic struck the world. The research community responded by expanding the research domain to encompass the perspective of managing the pandemic’s impact on SCs. Pioneering this avenue, Ivanov introduced the concept of SC survivability, revisiting existing SC models in light of the lessons derived from the pandemic [15,34].
  • Path 1: This first research path in Cluster 3 primarily centers around the COVID-19 pandemic, exploring strategies to contain and mitigate the repercussions of pandemics on SCs.
  • Path 2: The second research path is characterized by an extension into SC networks. This expansion is exemplified by publications such as Ivanov’s “Reconfigurable Supply Chain: The X-Network” and “Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak” [20,34].
  • Path 3: The third research path introduces emerging digital trends into the research landscape. Building upon the established simulation methods of Cluster 2, this path incorporates emerging digital trends like digital twinning, artificial intelligence, and machine learning. Noteworthy examples include D. Burgos’ “Food Retail Supply Chain Resilience [22,39] and the COVID-19 Pandemic: A Digital Twin-Based Impact Analysis and Improvement Directions” and Ivanov’s “A Digital Supply Chain Twin for Managing Disruption Risks and Resilience in the Era of Industry 4.0” [3,40], both from 2021.
A word cloud analysis was conducted to validate the described characteristics in each cluster, incorporating the titles, abstracts, and keywords of each publication assigned to a cluster (Figure 6). A high-level comparison of the generated word clouds shows that we are dealing with a SCM-centric research area, with ‘supply’, ‘chain’, and ‘disruption’ being the most frequent words across all clusters. To enable a focus on the other important words, we have deleted them for the word cloud creation process.
Figure 6. Detailed cluster definition and reflection in a word cloud by cluster.
Examining the other most frequently used words, the developmental progression outlined in the passage above becomes apparent. The word cloud of Cluster 1, defined as the foundation of the research area, is dominated by words such as ‘management’, ‘resilience’, and ‘research’. This confirms the previously stated definition of Cluster 1 as the foundation of this research area. The assumption that this cluster covers the research area’s basis is further reinforced by the appearance of the word ‘understanding’, frequently used together with ‘complexity’ in the papers. It can also be interpreted that this lack of understanding has pushed the usage of the word ‘simulation’ as a representative of simulation methods, comparing Clusters 1 and 2. By comparing the increase in the usage of the word ‘performance’ between Clusters 1 and 2, it can be suggested that after understanding the basic research scenarios in Cluster 1, the researchers took advantage of this and focused on increasing the performance of SCs through simulation techniques.
Turning to the word cloud of Cluster 2, the words ‘simulation’, ‘performance’, ‘ripple’, and ‘effect’ stand out. This aligns with the assertion that Cluster 2, the successor of Cluster 1, focuses on performance optimizations using various simulation methods. Further interest was also aroused by the word ‘politics’, which suggests that the research community has expanded its view of how SC disruption relates to political requirements. Compared to Cluster 1, it can also be stated that the word ‘management’ shrank. This suggests that the research community went beyond discussing basic management principles and redirected their focus on optimizing SCs under given management principles. The research focus changed again by comparing the increasing usage of the word ‘network’ from Cluster 2 to Cluster 3. Its rise from a nonexistent word in Cluster 2 to one of the most prominent ones in Cluster 3 supports the thesis that the research community has changed and adapted the developed practices for linear SCs into the upcoming world of SC networks.
In Cluster 3, the word ‘COVID’ and the related emphasis on the survivability of SCs come to the forefront. In addition, a new direction is indicated by the appearance of the word ‘network’. To provide an outlook for this, looking at the newly added words in Cluster 3 is interesting. By simply comparing the size, the most relevant ones are ‘adaptive’, ‘complex’, ‘COVID’, ‘firms’, ‘observed’, ‘systems’, ‘strategies’, and ‘twin’. It is obvious that the word ‘COVID’ will not be a dominating topic in future research, given that the pandemic is losing its momentum. Synthesizing the remaining words might give us a hint about future research directions. It seems that the research community will focus on complex SC network structures as a SC design principal and will develop new strategies to increase the adaptability of these SC networks to external disruptions, like COVID-19, under the usage of digital technologies (digital twins) as successors to the simulation methods.

4.3. Clusters’ Research Gaps and Opportunities

It can be observed that the open research areas presented in each cluster are interrelated. It has been identified that the research areas initially highlighted in Cluster 1 are explored further in Cluster 2, while the research areas from Cluster 2 are, in turn, expanded upon in Cluster 3. A common thread can be discerned in the chronological development of this scientific field. The following passage proves the scientific focus of each cluster by summarizing relevant publications. It provides a general overview of the research gaps and opportunities by giving a detailed overview in the corresponding tables.
Cluster 1 laid the foundation for this research path. C. Tan, in his publication “Perspectives in Supply Chain Risk Management”, confirmed the relevance of SC risk management by exploring disruptions in SCs using practical use cases and initially integrating them with quantitative simulation models [41]. This laid the groundwork for fundamental research areas within this domain. Subsequent investigations into supply management, demand management, product management, information management, and associated performance measures were deemed necessary. Sheffi and Rice also articulated a similar perspective in their article “A Supply Chain View of the Resilient Enterprise” [29]. This work analyzed relevant areas within an enterprise and demonstrated that the overall resilience of the enterprise can be enhanced by strengthening these areas with resilience capabilities. Another publication that reinforces Cluster 1 foundation is C. Craighead’s article “The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities”. In addition to defining relevant design parameters and mitigation capabilities to reduce disruptions in SCs, Craighead et al. [28] were one of the first to emphasize the need for simulation-based studies to analyze this context. This subsequently became the dominant research theme in Cluster 2. Besides these publications that underline the research focus of Cluster 1, all publications in Cluster 1 have been studied, and the identified research gaps and opportunities have been summarized (Table 10).
Table 10. Classification and summary of the gaps and opportunities in Cluster 1.
Exemplifying many publications in Cluster 2, Hosseini reviewed the recent research landscape of quantitative simulation techniques associated with SC disruptions [37]. His literature review points to nine open research avenues that primarily demand further investigation of simulation methods within SC disruptions. Building on this idea, A. Schmitt published her article “A Quantitative Analysis of Disruption Risk in a Multi-Echelon Supply Chain”. In this article, she describes her discrete-event simulation (DES) approach to managing disruptions through strategically placed inventory along a SC. Furthermore, she advocates for the more intensive use of simulation models in specific SC disruption scenarios [32]. As a third example, D. Ivanov postulated in 2013 that recent research in simulations related to SC disruptions is limited to handling specific detailed issues [20]. He called for a broader approach in this context, which, among other publications, laid the groundwork for further investigation into the usage of simulation techniques in the broader context of digital twinning, a dominant topic in Cluster 3. Details of the mentioned research spots and opportunities of all 25 papers in Cluster 2 can be reviewed in Table 11.
Table 11. Classification and summary of the gaps and opportunities in Cluster 2.
Following the primary goal of this research direction, a new research direction is available for exploration. This entails a deeper investigation into active reconfiguration strategies to effectively mitigate the ripple effect within SC networks [16,65,66]. Furthermore, an augmentation of insights gleaned from research paths that underscore the heightened utilization of digital trends merits attention [22,64,70]. This exploration inherently ushers in a fresh avenue of inquiry.
Another promising research trajectory, as proposed in [71], centers on the exploration of methods to contain exogenous shocks within a SC network, effectively curbing their impact at or near their point of origin. To elaborate, this research direction can be disaggregated into three consecutive research tasks.
The initial task involves building upon Liaguno Arrate’s foundational work, which presents a state-of-the-art conceptual framework and simulation techniques to preempt the ripple effect in SCs [11]. This endeavor necessitates a more profound exploration of the pre-emptive and reactive measures stipulated therein, culminating in the creation of a comprehensive catalog of measures.
The second task entails delving deeper into the modeling of SC networks using simulation techniques, as Olivares-Aguila and ElMaragghy advocate [71]. This phase involves the meticulous selection and application of simulation techniques, facilitating a more robust understanding of SC network dynamics.
Once the potential simulation techniques have been exhaustively examined and the relevant measures for containing exogenous shocks in SC networks have been delineated, the third task can be initiated. This phase involves developing a foundational model for a SC network and, subsequently, exploring the applicability of individual measures or combinations thereof to mitigate the ripple effect within SC networks effectively.
This comprehensive research approach will undoubtedly pave the way for a richer understanding of how exogenous shocks can be managed effectively within SC networks, contributing significantly to the advancement of the field [72]. Details on the research gaps in Cluster 3 can be seen in the following table (Table 12).
Table 12. Classification and summary of the gaps and opportunities in Cluster 3.

4.4. Usage of Simulation Methods in the Clusters

After analyzing the clusters based on the open research avenues they point to, another relevant finding is how simulation methods have developed within these clusters. Since the clusters build upon each other chronologically, understanding how simulation methods usage has generally evolved is of greater interest.
What can be observed is a steady increase in the usage of simulation methods. In Cluster 1, only 14 percent of the published papers actively employed simulation methods. This percentage increased to 39 percent in Cluster 2 and 50 percent in Cluster 3.
When examining the specific simulation techniques used in this research area, comparing the overall structure of employed simulation techniques reveals that discrete-event simulation (DES) and system dynamics (SD) are the most frequently utilized methods. Agent-based modeling (ABM) and control theory (CT) appear to play a minor role in simulating disruptions in SCs.
Looking at the individual development of the usage of each simulation method, it is noteworthy that the utilization of DES increases from Cluster 1 to Cluster 2, reaching its peak in this time series. In Cluster 3, the high value of 67 percent usage seen in Cluster 2 could not be reached again, but a substantial usage rate of 57 percent for all simulation-related publications was achieved.
Analyzing the usage of SD across all clusters, it can be observed that the peak occurred in Cluster 1. Subsequently, the trend declined in Cluster 2 but rose again, reaching a 29 percent usage rate, equivalent to the usage of DES.
The usage of ABM and CT remains relatively low. Both methods started with an overall usage rate of 20 percent across all simulation-related publications but declined to 11 percent in Cluster 2. In Cluster 3, ABM was utilized in only one publication, and CT was not employed in this research area.
In conclusion, DES and SD are the leading and most promising simulation methods in this scientific area (Table 13).
Table 13. Simulation methods used by the cluster.
Taking a deeper look into the simulations conducted by individual scientists, D. Ivanov stands out as the most frequent user of simulations in this research area, with seven simulation-related publications. A. Schmitt, who has published two simulative approaches to analyze SC disruptions, takes the second rank, followed by seven scientists who have each published 1 simulation-related paper.
Examining the evolution of simulation methods usage by Ivanov, in Cluster 2, three publications utilized a DES approach, in addition to one publication employing a SD Simulation and another using CT. In Cluster 3, Ivanov exclusively published papers employing DES, perhaps because he considers it as having the most potential among simulation techniques.
It can also be noted that Ivanov’s peak in publications occurred in Cluster 2. However, this observation should consider that Cluster 2 had a longer duration than Cluster 3. Therefore, it is reasonable to expect that we will see more publications from Ivanov in Cluster 3.
DES and SD are the leading methods for simulating SC disruptions. The scientific community is currently engaged in a controversial discussion on whether SD or DES is the superior simulation approach in SCM. There is no clear consensus on which simulation method should be used in which context [77]. It appears that the main driver for this decision is the personal expertise and preference of the respective author [78]. However, scientific society has established a common understanding regarding using DES and SD. DES is generally employed to investigate problems at an operational level [78,79,80,81], whereas SD is predominantly used for strategic analysis [77,82]. This raises the question: Do authors follow the common understanding of the research community, using SD to simulate strategic SC problems and DES for tactical and operational simulation challenges? Or is the choice of using one simulation tool over the other explained by their personal preference?
To determine whether authors choose simulation methods for a problem based on personal preference, an analysis of all existing publications by authors has been conducted. All publications were identified using the WoS author search, forming the basis for a comprehensive literature review. Titles, keywords, and abstracts were screened for the relevant simulation methods, specifically DES and SD. In cases where neither method was used in a publication, further scrutiny was applied to determine if the context of the simulation was addressed. The results affirm the assertion made by Tako and Robinson [77] that the primary decision driver for choosing one simulation method over the other is the personal expertise and preference of the individual author. To illustrate, A. Grossler has written ten publications, with six referencing SD and none based on DES methods. More insightful examples are found in the publication histories of J. Swanson and A. Dolgui. In Cluster 3, 15 percent of Swanson’s publications are directly connected to SD, while a minor portion (below 3 percent) is related to DES. A similar pattern is observed in the publication history of A. Bueno-Solano, where approximately 75 percent of the publications reference SD, such as those found in Cluster 1. Considering these results, it can be concluded that choosing a particular simulation method is likely linked to the author’s preference.

5. Conclusions

This systematic literature review aimed to explore the use of simulation tools in managing supply chain disruptions by addressing several research questions: the historical evolution of the field, the prevalent simulation methods, specific challenges addressed, and the identification of research gaps. Our review of 213 publications revealed a robust and evolving academic landscape characterized by significant milestones and contributions from key authors in the field. The chronological analysis highlighted simulation methods’ progressive complexity and sophistication, from DES and agent-based modeling to SD. These methods have been pivotal in addressing various supply chain challenges, such as predicting disruptions, optimizing network configurations, and enhancing decision-making processes.
In addressing the first research question about the historical evolution of simulation in supply chain disruption management, our findings illustrate a gradual yet significant shift towards more complex and integrated simulation approaches. From 2000 to 2005, research focused on using discrete-event simulation (DES) to analyze supply chain dynamics and identify bottlenecks. Between 2005 and 2010, the focus shifted to agent-based modeling (ABM) and system dynamics (SD) to study complex supply chain systems. From 2010 to 2015, the emphasis was on strategic reconfiguration of supply chain networks using DES, ABM, and SD to enhance resilience. Since 2015, there has been a growing interest in digital technologies (e.g., digital twins, AI) for real-time monitoring and control of supply chains. Overall, research has evolved from basic modeling approaches to more complex simulations, combining various simulation techniques, and digital strategies, emphasizing network reconfiguration and technological advancements.
Regarding the second research question on the prevalent simulation methods, our analysis identified DES, ABM, and SD as the most relevant ones. ABM and SD are the most frequently employed techniques in recent studies. These methods offer robust frameworks for capturing the dynamic behaviors and interactions within supply chains, making them particularly suitable for studying disruptions and their cascading effects.
The third research question focused on specific challenges addressed by these simulation tools. Our review found that simulations are primarily used to tackle issues such as supply chain resilience, risk mitigation, and optimization of recovery strategies. DES excels in modeling operational processes, managing inventory, and enhancing responsiveness to disruptions. SD provides insights into long-term behavior, identifying bottlenecks and analyzing the impacts of demand or supply changes over time. ABM offers a perspective on individual agent behaviors, facilitating the analysis of complex interactions among suppliers, manufacturers, and other stakeholders. Finally, hybrid simulation integrates these approaches to provide a comprehensive understanding of supply chain networks, combining detailed process modeling with strategic feedback loops for enhanced decision-making and performance optimization. These tools have been instrumental in enabling researchers and practitioners to explore various disruption scenarios and develop effective mitigation strategies, thus enhancing the overall robustness of supply chains.
Regarding identifying research gaps posed by the fourth research question, our review highlighted several areas needing further exploration. Notably, there is a call for more dynamic analysis and control theory applications to understand disruptions’ temporal aspects better. Additionally, integrating multiple simulation methods to address complex, multi-faceted disruption scenarios remains underexplored. Addressing these gaps could provide deeper insights and more comprehensive solutions for managing supply chain disruptions. Two primary research frontiers were identified: strategic reconfiguration of supply chain networks and the rapid implementation of countermeasures. The first frontier focuses on designing resilient supply chains that can withstand and quickly recover from disruptions. In contrast, the second emphasizes the need for swift, effective responses to minimize the impact of unforeseen events. These frontiers align with the growing recognition of supply chain resilience as critical to global economic stability and sustainability. Despite these advancements, this review also highlighted several research gaps. There is a need for more dynamic analysis and control theory applications to understand better the temporal aspects of disruptions and their propagation through supply chains. Additionally, future research should explore the potential of combining multiple simulation methods to address complex, multi-faceted disruption scenarios comprehensively.
In conclusion, this study provides a detailed overview of the current research on simulation tools for supply chain disruption management. It underscores the importance of continued innovation and integration of advanced technologies to build more resilient and sustainable supply chains. By addressing the identified research gaps, future studies can further enhance our understanding and capabilities in this critical area, contributing to global efforts toward achieving the United Nations’ Sustainable Development Goals. Specifically, improving supply chain resilience directly supports SDG 9 (Industry, Innovation, and Infrastructure) by fostering resilient infrastructure and promoting inclusive and sustainable industrialization. Furthermore, enhancing supply chain robustness contributes to SDG 12 (Responsible Consumption and Production) by ensuring sustainable consumption and production patterns. This research advances academic knowledge and provides practical insights that align with the global sustainability agenda, facilitating a more sustainable and resilient future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16145957/s1, File S1: PRISMA Statement. Ref. [83] is cited in the Supplementary File.

Author Contributions

Conceptualization, B.K., J.M. and M.K.; methodology, B.K., J.M. and M.K.; software, B.K.; validation, B.K., J.M. and M.K.; formal analysis, B.K., J.M. and M.K.; investigation, B.K.; resources, B.K., J.M. and M.K.; data curation, B.K.; writing—original draft preparation, B.K.; writing—review and editing, B.K., J.M. and M.K.; visualization, B.K.; supervision, J.M. and M.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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