artificial intelligence in supply chain management a systematic literature review

• Multiplexing
• Information Technology
• Information Systems
• Broadcast Technology
• Information Systems (Business Informatics)

Artificial intelligence in supply chain management: A systematic literature review

• September 2020
• Journal of Business Research 122(January 2021):502-517
• 122(January 2021):502-517

• Copenhagen Business School

• National University of Ireland, Maynooth
• This person is not on ResearchGate, or hasn't claimed this research yet.

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Dattatreya Mukherjee

• Suriya Narayanan Harikrishnan
• Dany José Cárdenas-Romay
• Andrés Mauricio Navarrete Ramos
• MANAGE DECIS

• Seyed Mohammad Mahdi Hamidi
• Seyed Farzad Hoseini
• Mohammad Kananizadeh

• A Seetharaman
• Koilakuntla Maddulety
• Mengyun Zhang
• Wenhua Wang
• J Manuf Tech Manag

• Adewale Hassan
• Raymond Yaw Sitsofe Agidi
• Chinedu Joseph Ezeh
• Olanrewaju Adebola Lawal
• MD Baniamin Sarder
• Jagjit Singh
• Meenakshi Yadav

• IND MARKET MANAG

• Rina Dechter
• Surabhi Verma

• INT J INFORM MANAGE
• Rohit Nishant

• J Retailing Consum Serv

• Hyejeong Kim
• Carol Centrallo
• INT J PROD ECON

• Benjamin T. Hazen
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Browse Econ Literature

• Working papers
• Software components
• Book chapters
• JEL classification

More features

• Subscribe to new research

RePEc Biblio

Author registration.

• Economics Virtual Seminar Calendar NEW!

Artificial intelligence in supply chain management: A systematic literature review

• Author & abstract
• 24 References
• 42 Citations
• Most related
• Related works & more

Corrections

• Toorajipour, Reza
• Sohrabpour, Vahid
• Nazarpour, Ali
• Oghazi, Pejvak
• Fischl, Maria

Suggested Citation

Download full text from publisher, references listed on ideas.

Follow serials, authors, keywords & more

Public profiles for Economics researchers

Various research rankings in Economics

RePEc Genealogy

Who was a student of whom, using RePEc

Curated articles & papers on economics topics

Upload your paper to be listed on RePEc and IDEAS

New papers by email

Subscribe to new additions to RePEc

EconAcademics

Blog aggregator for economics research

Cases of plagiarism in Economics

About RePEc

Initiative for open bibliographies in Economics

News about RePEc

Questions about IDEAS and RePEc

RePEc volunteers

Participating archives

Publishers indexing in RePEc

Privacy statement

Found an error or omission?

Opportunities to help RePEc

Get papers listed

Have your research listed on RePEc

Open a RePEc archive

Have your institution's/publisher's output listed on RePEc

Get RePEc data

Use data assembled by RePEc

HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on.

• failed: apalike

Authors: achieve the best HTML results from your LaTeX submissions by following these best practices .

AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis

Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques, revolutionizing predictive capabilities and risk mitigation strategies. The significance of this evolution stems from the critical role of robust risk management strategies in ensuring operational resilience and continuity within modern supply chains. Previous reviews have outlined established methodologies but have overlooked emerging AI/ML techniques, leaving a notable research gap in understanding their practical implications within SCRA. This paper conducts a systematic literature review combined with a comprehensive bibliometric analysis. We meticulously examined 1,717 papers and derived key insights from a select group of 48 articles published between 2014 and 2023. The review fills this research gap by addressing pivotal research questions and exploring existing AI/ML techniques, methodologies, findings, and future trajectories, thereby providing a more encompassing view of the evolving landscape of SCRA. Our study unveils the transformative impact of AI/ML models, such as Random Forest, XGBoost, and hybrids, in substantially enhancing precision within SCRA. It underscores adaptable post-COVID strategies, advocating for resilient contingency plans and aligning with evolving risk landscapes. Significantly, this review surpasses previous examinations by accentuating emerging AI/ML techniques and their practical implications within SCRA. Furthermore, it highlights the contributions through a comprehensive bibliometric analysis, revealing publication trends, influential authors, and highly cited articles. This analysis provides an encompassing view of the field’s growth, offering insights into its evolution. This comprehensive review provides a roadmap for practitioners and researchers, offering insights into fortifying supply chain risk management strategies through AI integration, ultimately contributing to a deeper understanding of evolving trends and applications in this dynamic field.

[inst1]organization=Department of Industrial Engineering and Management,addressline=Khulna University of Engineering and Technology (KUET), city=Khulna, postcode=9203, country=Bangladesh

[inst2]organization=Department of Computer Science,addressline=American International University-Bangladesh (AIUB), city=Dhaka, postcode=1229, country=Bangladesh

1 Introduction

Supply chain management (SCM) has long grappled with various challenges stemming from a spectrum of sources. Past outbreaks of infectious diseases, geological upheavals like earthquakes, and other natural catastrophic events have put supply chains at risk, albeit on a limited scale (Govindan et al.,, 2020 ) . These incidents have illustrated how interrelationships within supply chains have formed intricate risk contagion networks, making them susceptible to contagion effects (Agca et al.,, 2022 ) . The cascade effect, characterized by risk spillover from one enterprise to another, exacerbates the challenges, culminating in a chain reaction of Supply Chain Resilience (SCR) disasters (Roukny et al.,, 2018 ) .

Across the globe, companies confront formidable challenges across all stages of their supply chains. Suppliers failing to meet delivery obligations, unpredictable shifts in customer demands, and episodes of panic buying are just a few examples of the hurdles companies face (Ivanov,, 2020 ) . In pursuing market leadership, enterprises must also contend with the complexity introduced by the management and launch of innovation projects, further complicating SCM (Kwak et al.,, 2018 ) . Furthermore, digital transformation has ushered in a wave of technological advancements in supply chain operations (Kwak et al.,, 2018 ) .

However, the most significant disruption in recent times emerged during the COVID-19 pandemic, impacting global supply chains profoundly. The disruptions encompassed not only the movement of people but also the flow of raw materials and finished goods, along with extensive disruptions in factory and supply chain operations (Sheng et al.,, 2021 ) . These disruptions ushered in unprecedented challenges for supply chain professionals as they grappled with an entirely new reality (Araz et al.,, 2020 ; Craighead et al.,, 2020 ) . The pandemic represents a distinctive form of supply chain disruption, distinct from natural or man-made disasters (e.g., the Japanese earthquake and 9/11 attacks) and disruptions driven by evolving technologies and changing customer attitudes (Zhang et al.,, 2020 ) .

While existing guidance exists for predicting, managing, and responding to these disruptions, the challenges brought about by COVID-19 have underscored the paramount importance of risk management, unlike any previous disruption (Bode et al.,, 2011 ; Craighead et al.,, 2020 ) . Consequently, the concept of SCR in the context of COVID-19 has emerged as a critical area necessitating further exploration and development.

Supply chains are not the sole victims in this precarious environment, as the ripple effect extends to upstream and downstream enterprises. Practical risk management programs are imperative for enterprises to avert SCR and enhance SCR management (SCRM). Moreover, incorporating advanced AI technologies, such as ML, can provide predictive capabilities for SCRM. ML algorithms have showcased their ability to identify abnormal risk factors and derive predictive insights from historical data (Guo et al.,, 2021 ; Mohanty et al.,, 2021 ) . By harnessing ML, enterprises can detect risk factors and anticipate market demands and potential risk scenarios (Punia et al.,, 2020 ; Wu et al., 2022a, ) . ML’s proficiency in handling non-linear relationships further bolsters its superiority over traditional linear models. Additionally, its aptitude for processing unstructured data, a challenge where traditional models falter, positions ML as a formidable tool for addressing time, cost, and resource constraints within supply chains.

However, despite its immense potential, supply chain researchers have historically exhibited limited familiarity with ML in comparison to other SCM aspects like mathematical programming and stochastic optimization (Liu et al.,, 2019 ; Shahed et al.,, 2021 ) . Furthermore, the classification of ML algorithms remains unclear (Janiesch et al.,, 2021 ; Xu and Jackson,, 2019 ) . Bridging this knowledge gap and exploring the value of ML for SCRM through interdisciplinary integration is a critical research need. Remarkably, there has been no prior effort to scrutinize the SCRM literature within an ML environment, in stark contrast to previous reviews primarily centered on risk definition, classification, and management strategies.

Companies should augment their analytical capabilities by harnessing organizational knowledge to enhance SCR, thereby elevating their information capabilities (Wong et al.,, 2020 ) . As underscored by previous studies, the role of AI-enabled technologies extends to promoting innovations for enhanced supply chain performance (SCP) ( Baryannis et al., 2019b, ; Nayal et al.,, 2021 ) . The adaptation capabilities and information processing prowess offered by AI techniques hold the potential to enhance SCP ( Belhadi et al., 2021a, ) . Notably, AI has found application across diverse sectors, improving flexibility and communication and reducing undue fluctuations for successful project execution (Lalmi et al.,, 2021 ) . To navigate the impact of risks and disruptions, (Katsaliaki et al.,, 2022 ) advocate the integration of three critical facets: long-term partnerships, IT applications for business enhancement, and government policies that facilitate adaptability.

The contemporary supply chain landscape demands the integration of AI, specifically ML, to invigorate SCRA and SCRM. While traditional methods possess merit, their limitations can be effectively addressed by AI, which offers predictive capabilities, nonlinear relationship analysis, and unstructured data processing. As supply chains continue to evolve, AI-based risk assessment is imperative for fostering resilience and sustaining the efficiency and effectiveness of supply chain operations. Existing reviews of SCRA using ML often fall short of offering a comprehensive view of evolving ML techniques, neglecting emerging trends and overlooking diverse applications and non-journal sources. They also tend to overlook the role of ML in the response phases of SCRM and do not sufficiently address data-related challenges.

In light of the limitations associated with traditional SCRA methods, there is a growing interest in harnessing the potential of AI techniques to enhance risk assessment practices. This paper presents a systematic literature review (SLR) focused on the application of AI in SCRA. The main objective of this review is to analyze the existing literature critically, identify research gaps, and provide insights into the use of AI techniques, such as ML, deep learning(DL), and natural language processing(NLP), to improve the accuracy and effectiveness of SCRA. Our paper has the following contributions-

Our study significantly contributes to the literature by conducting a comprehensive review and employing bibliometric and cocitation analysis to assess the application of AI and ML techniques in SCRA, specifically focusing on the most recent papers.

This research addresses the evolving landscape of SCRA by considering the challenges posed by post-COVID uncertainties and emphasizing the increasing significance of AI in risk assessment.

This systematic review paper addresses the evolving landscape of SCRA by considering the challenges posed by post-COVID uncertainties and emphasizing the increasing significance of AI in risk assessment.

Our paper highlights pivotal trends and key findings, shedding light on the current landscape of AI/ML in SCRA.

We offer valuable insights into the next steps for researchers and practitioners, guiding the evolution of SCRA methods in an ever-changing world.

Our review aids academics and industry professionals seeking effective risk management strategies in today’s dynamic supply chain environment.

This review follows a structured paper skeleton, starting with an introduction to the topic and its significance in the field of SCM in the “ Introduction ” section. Following this, a comprehensive “ Literature Review ” unfolds, delving into the existing body of knowledge. The subsequent “ Methodology ” section outlines the study’s design, including article search strategies, selection criteria, and the synthesis of gathered data. Complementing these fundamental segments, the paper integrates a specific section for “ Bibliometric Analysis ” Utilizing bibliometric tools, this section showcases insights distilled from a comprehensive analysis of scholarly publications, focusing on AI in SCRA. Visualizations and key observations derived from this analysis are encapsulated here. Transitioning forward, the narrative navigates through diverse AI techniques deployed in SCRA, expounded within the “ AI Techniques for Supply Chain Risk Assessment ” section. Here, the discussion encapsulates the advantages, limitations, and real-world applications of these techniques. Moreover, it underlines the “ Managerial Implications ”, providing actionable insights and recommendations for industry practitioners based on the reviewed literature. This section delineates practical implications derived from the reviewed scholarly material aimed at aiding managerial decision-making within SCRA contexts. Continuing, the paper addresses the “ Challenges and Limitations ”encountered in this field, and forecasts potential “ Future Research Directions ” This forward-looking section paves the way for continued exploration and advancement within this domain. Ultimately, the review culminates in a comprehensive “ Conclusion ”, summarizing the primary findings drawn from the review process. Additionally, it furnishes recommendations tailored for both practitioners and researchers aimed at leveraging the full potential of AI in SCRA.

This meticulously structured approach endeavors to contribute to existing knowledge and bridge critical gaps within the literature. It is designed to furnish invaluable insights for academia and industry stakeholders alike.

2 Literature Review

Deiva Ganesh and Kalpana, ( 2022 ) conducted a comprehensive and descriptive literature study to identify AI and ML approaches in SCRM stages. This study examined SCRM research publications from three scientific databases from 2010 to 2021. They proposed a data analytics, simulation, and optimization framework that might produce a comprehensive Supply Chain risk identification, assessment, mitigation, and monitoring strategy. Hence, leveraging AI, Blockchain, and the Industrial Internet of Things (IIoT) to create smarter supply chains can transform how firms handle uncertainty. This analysis of (Deiva Ganesh and Kalpana,, 2022 ) does not evaluate blockchain, big data, and IIoT-related supply chain articles, which may limit information. Due to limited research, only recent English-language publications on SCRM and AI were included. Nimmy et al., ( 2022 ) did a thorough literature evaluation on operational risk assessment methods and whether AI explains them. The report suggested that risk managers use auditable supply chain operational risk management methodologies to understand why they should take a risk management action rather than just what to do. While they employed the SLR method, they could only examine some AI solutions for SCRM. They suggested hybridizing supply chain operational risk management and explainable AI (XAI) to obtain XAI-like characteristics in future research. Li et al., ( 2023 ) analyzed COVID-19 SCR research history, present, and future. In particular, supervised ML classifies 1717 SCR papers into 11 subject categories. Each cluster was then studied in the context of COVID-19, indicating three related skills (interconnectedness, transformability, and sharing) on which enterprises could work to develop a more resilient supply chain post-COVID. Their data only came from Scopus’ core collection, which might affect the results; thus, they suggested adding WoS and EBSCO to the evaluation Xu et al., ( 2020 ) . Given the fast expansion of SCR research, they only picked English-language articles, which may have excluded valuable knowledge. They recommended network analysis to determine cluster linkages and SCR literary themes.

Naz et al., ( 2022 ) examined the role of AI in building a robust and sustainable supply chain and offered optimal risk mitigation options. For review, 162 SCOPUS research publications were selected. Based on the nominated articles, Structural Topic Modeling was used to produce various AI-related theme topics in SCR. AI research trends in SCR were examined using R-package bibliometric analysis. They solely studied journal articles, not conference papers, field reports, corporate reports, book chapters, etc. Ni et al., ( 2020 ) analyzed articles from 1998/01/01 to 2018/12/31 in five major databases to highlight the newest research trends in the field. ML applications in SCM were still developing due to a lack of high-yielding authors and poor publication rates. 10 ML algorithms were extensively utilized in SCM, but their utilization was unequal among the SCM tasks most often reported. This paper has limitations in reviewing only five popular databases to limit articles for evaluation that might have filtered some related articles. Second, only widely used ML methods in SCM were counted in this review, and other ML techniques brought to SCM may be helpful later on. Low-frequency ML algorithms should be analyzed for further research in this area. Finally, their article contained 32 well-recognized ML methods; however, some newly generated ML algorithms may be used in SCM after 2015. Baryannis et al., 2019b examined supply chain risk definitions and uncertainty. Then, a mapping analysis categorizes available literature by AI approaches and SCRM tasks. Most of the examined works focus on building and assessing a mathematical model that accounts for various uncertainties and hazards but less on establishing and analyzing the applicability of the suggested models. They found that only 9 of 276 research (3%) use comprehensive techniques that cover all three SCRM stages (identification, assessment, and response). Yang et al., ( 2023 ) thoroughly examined the advancement of ML algorithms in SCRM by gathering 67 publications from 9 authoritative databases in the first half of 2021. They analyzed only English language journal articles from 9 relevant academic databases, excluding conference papers, textbooks, and unpublished articles and notes. This analysis relied on keyword searches, which may have missed some work. Schroeder and Lodemann, ( 2021 ) used a comprehensive and multi-vocal literature study to get a complete picture of our subject field. The SLR identified 533 papers in this area and examined 23. The comprehensive literature study found that just a few examples of ML in SCRM have been reported in depth in the scientific literature, and those examples focus on manufacturing, transport, and the complete supply chain. Shi et al., ( 2022 ) examined 76 works on credit risk utilizing statistical, ML, and DL methods over the last eight years. They offered a unique classification approach and performance rating for ML-driven credit risk algorithms utilizing public datasets. Data imbalance, dataset inconsistency, model transparency, and DL model underuse are discussed. Their review found that most DL models outperform standard ML and statistical algorithms in credit risk prediction, and ensemble techniques outperform single models.

Research Questions We formulated the following research questions to guide our SLR:

What is the current state of research on applying AI/ML techniques in SCRM?

Which AI/ML techniques are commonly employed in supply chain risk assessment, prediction, and mitigation?

What are the key findings and trends identified in the literature regarding using AI/ML in SCRM?

What are the research gaps and future directions in this field?

3 Methodology

3.1 study design.

The research utilized a systematic approach to conduct a comprehensive literature review coupled with a bibliometric analysis. This methodology aimed to scrutinize the intersection of AI/ML techniques and SCRA, providing insights into research trends, methodologies, and emerging themes.

The SLR followed a robust methodology, adhering to the guidelines proposed by (Denyer and Tranfield,, 2009 ) . This approach aimed to identify and analyze research articles focusing on integrating AI/ML techniques within SCRA.

3.2 Search Strategy

We conducted a comprehensive search using two major academic databases: Google Scholar and Web of Science (WoS). We used the ”Publish or Perish 8” tool for the Google Scholar search to filter articles published between 2015 and 2023. The initial search yielded 1000 articles. In the WoS search, we employed the following search strings to retrieve relevant articles:

(ALL=((”supply chain”) AND (”risk*” OR ”credit risk*” OR ”risk assessment*” OR ”risk prediction” OR ”disruption*” OR ”disease outbreak*” OR ”post COVID resilienc*”) AND (”artificial intelligence” OR ”machine learning” OR ”neural network*” OR ”deep learning” OR ”reinforcement learning” OR ”SVM” OR ”support vector machine” OR ”boosting” OR ”ensemble” OR ”bayesian network model” OR ”random forest” OR ”LSTM” OR ”long short-term memory”))) NOT (DT==(”PROCEEDINGS PAPER” OR ”BOOK CHAPTER” OR ”EDITORIAL MATERIAL” OR ”LETTER”))

The WoS search initially retrieved 434 articles.

3.3 Article Selection Process

We followed a systematic approach to select the most relevant articles for our analysis. The inclusion and exclusion criteria were applied sequentially to ensure the selection of high-quality research. The following steps were taken:

Removal of Duplicates: We removed duplicate articles identified between the two databases, resulting in 360 duplicates.

Filtering by Reliable Publishers: We only considered articles published by reliable and reputable publishers, excluding 398 articles.

Non-English Exclusion: We excluded articles not written in English, resulting in removing 3 articles.

Filtering by Publication Type: We excluded articles classified as Q3 or Q4, proceedings papers, book chapters, editorials, and letters, resulting in the removal of 244 articles.

Title, Abstract, and Conclusion Review: We reviewed the remaining articles based on their title, abstract, methodology, and conclusion to ensure their relevance to our research questions.

This step removed 386 articles, leaving us with a final set of 48 research articles.

3.4 Data Extraction and Synthesis

The selected articles underwent detailed analysis, focusing on methodologies, AI/ML techniques employed, key findings, and implications for SCRA. Data extraction forms were filled in to gather pertinent information for synthesis and further analysis.

To understand the research landscape comprehensively, we performed bibliometric and cocitation analysis of the 434 articles resulting from the filtering process. The bibliometric analysis helped us identify the field’s most influential authors, journals, and key research themes. Cocitation analysis allowed us to identify clusters of related articles and determine their interconnections.

Our study has several limitations that should be acknowledged. First, the search was limited to articles published between 2015 and 2023, and it is possible that relevant articles published before or after this timeframe were not included.

4 Bibliometric Analysis

In our quest to comprehensively analyze the landscape of supply chain risk assessment using machine learning, we employed an array of tools, including MS Excel and VOSviewer software. This bibliometric analysis comprised several key components, each contributing to a deeper understanding of the field.

4.1 Publication Trend Over Time

Figure  2 offers a dynamic portrayal of the publication trend over the years, presenting an invaluable temporal dimension to our bibliometric analysis. A meticulous examination of data showed how the research landscape has evolved from 2014 to 2023. This longitudinal perspective equips us with the knowledge of emerging trends, shifts in focus, and the growth trajectory of the field.

4.2 Geographical Distribution of Research

In Figure  3 , we present a geographical distribution of research related to SCRA using machine learning. The figure offers a clear visualization of countries that have actively contributed to the literature in this field. The deeper coloration signifies a higher volume of research publications from these regions. This representation enriches our understanding of the global landscape of SCRA, highlighting regions with significant scholarly contributions.

4.3 Analysis of Authors

Table   1 presents a ranking of the top 10 authors in the field of risk prediction and analysis based on the number of research papers attributed to each. ”Gupta S” leads the list with 10 papers, highlighting a substantial body of work in this domain. This table serves as a concise reference for identifying influential authors who have made significant contributions to the field of risk analysis and provides valuable insights into their scholarly output, aiding researchers and professionals in exploring their work.

4.4 Publisher Contributions

Figure  4 provides a compelling visual representation of the leading publishers in the field of SCRA, with a specific emphasis on applications of AI and ML. This chart clearly depicts the number of research papers associated with each publisher, enabling researchers and professionals to discern the most prolific contributors to this dynamic domain. It serves as a valuable resource for identifying key publishers that disseminate influential research at the intersection of AI/ML and SCRA, granting a panoramic view of the publication landscape in this evolving field.

4.5 Journal Contributions

This bar chart, represented in Figure  5 , showcases the top 11 journals significantly contributing to the field of SCRA with a specific focus on AI and ML applications. This visual representation helps researchers and professionals identify the key journals for accessing relevant and impactful research in AI/ML and SCRA. It concisely overviews this dynamic field’s most prolific journal sources.

4.6 Co-citation Analysis

In pursuing a comprehensive understanding of the scholarly landscape in AI-based SCRA, we present three co-citation analyses derived from bibliometric data of the authors, cited references, and publication sources using VOSviewer. Each visualization offers unique insights into collaboration, thematic prominence, and citation patterns within this dynamic field.

Figure  5(a) illustrates connections between authors, highlighting 55 out of 18,858 authors surpassing a minimum citation threshold of 20. These authors formed three distinct clusters based on their co-citation patterns: Red (22 items), Green (19 items), and Blue (14 items). The clusters signify groups of authors who are frequently cited together, suggesting shared research themes or collaboration networks. Node size signifies citation counts, and connections depict co-citation relationships. This visualization unveils collaborative networks and shared contributions among influential authors.

The co-citation analysis of cited references involved a minimum citation threshold of 10, identifying 58 cited references meeting this criterion (Figure 5(b) ). These references formed five distinct clusters, reflecting thematic relationships among the cited works. The clusters shed light on the interconnectedness of references, providing insights into key themes or influential works within the field. Five formed clusters and individual nodes within each analysis were carefully examined for meaningful patterns. Each cluster represents distinct research themes or methodologies, while individual nodes signify specific authors, countries, or references that play pivotal roles in the overall co-citation network.

A minimum of 50 citations per source was set to analyze publication sources, identifying 70 sources meeting the criteria (Figure 5(c) ). These sources were grouped into three clusters based on their co-citation patterns. These clusters offer insights into the thematic literature concentrations within your field of study. The three formed clusters were examined to discern the major themes and sub-fields represented by the co-cited sources. The significance of central sources was considered within each cluster, as they represented foundational works or widely acknowledged references in the field.

4.7 Co-authorship Analysis

Co-authorship analysis is a method to explore and understand collaborations among authors or researchers based on their joint publication activities. This analysis highlights the collaborative essence inherent in this research domain, revealing influential partnerships among authors and the noteworthy contributions of specific countries.

Figure 6(a) unveils the co-authorship network among authors within this field. By setting a threshold of at least 3 documents per author, we identified 63 authors meeting this criterion. The resulting visualization, created using VOSViewer, showcased 6 distinct colored clusters. For instance, clusters colored red, green, and deep blue comprised 10, 7, and 5 authors, respectively. The clusters are formed based on similar collaborative behaviors among these authors. The links connecting authors within clusters represent the robustness of their collaboration, indicating frequent joint authorship or shared research endeavors.

Figure 6(b) demonstrates co-authorship analysis at a country level. Using data encompassing document count, citations, and total link strength, a minimum document threshold of 2 per country identified 47 countries meeting this criterion. The visualization prominently emphasizes China’s substantial dominance across document count, citations, and total link strength compared to other countries. This dominance underscores China’s extensive contributions to the realm of Supply Chain Risk Assessment utilizing AI/ML techniques.

4.8 Co-occurrence Analysis

The results of our co-occurrence analysis using VOSviewer, focusing on the unit of analysis as ’Authors’ and ’Keywords Plus,’ were presented in Figure  8 , offering insights into the thematic intricacies of SCRA and ML. The co-occurrence analysis is a powerful tool to reveal latent connections and thematic clusters within the expansive landscape of scholarly literature. VOSviewer facilitated the exploration of how certain keywords converge within the existing body of literature. This visualization enables us to discern the prevalent themes and areas of emphasis within this research space, guiding our understanding of the critical topics that have garnered scholarly attention.

In Figure 7(a) , the network map illustrates the relationships among these keywords based on their co-occurrence within the authorship data. Each node represents a keyword, and the links between nodes signify the strength of co-occurrence. Larger nodes indicate keywords with higher occurrences, while the proximity of nodes reflects the degree of association. In this analysis, 1483 keywords were initially considered, but after applying a minimum threshold of 4 occurrences, 44 keywords emerged as frequent keywords.

In Figure 7(b) , the analysis was conducted by setting a minimum threshold of 10 occurrences for each keyword, identifying 42 keywords that surpassed this criterion from a total pool of 1032 keywords. The 42 keywords meeting the established threshold were subjected to clustering analysis, unveiling intricate co-occurrence patterns. These 4 clusters signify prevalent themes and provide insights into the interconnectivity of concepts within our research domain.

4.9 Top 10 Affiliations

Table   2 provides an overview of the top 10 affiliations based on paper count in the field of SCRA. These affiliations have significantly contributed to the literature, collectively accounting for a notable portion of the total papers reviewed (2.995% to 1.613%). The table underscores the involvement of various academic and research institutions in advancing research related to SCRA.

4.10 Bibliographic Coupling

Bibliographic coupling is a method in bibliometrics that assesses the similarity between documents based on their shared references. This approach identifies connections and relationships among scholarly works by analyzing the overlap in their citation patterns. In other words, if two documents cite a similar set of references, they are considered to be bibliographically coupled. This analysis helps reveal thematic associations, collaborative efforts among authors, and the intellectual structure of a research field. Clusters formed through bibliographic coupling indicate groups of documents, authors, countries, organizations, or sources that share common references, suggesting a degree of interrelatedness in their research content. The strength and nature of these couplings provide valuable insights into the structure and dynamics of academic knowledge networks.

4.10.1 Authors

In Figure  9 , we identified 39 significant authors out of 1407, forming six distinct clusters based on a minimum document threshold of 3 per author. These clusters represent cohesive groups of authors who share common research interests, as evidenced by the co-citation of their works. Here influential contributors like ”Gupta, Shivam,” ”Ivanov, Dmitry,” ”Modgil, Sachin,” ”Qu, Yingchi,” ”Wang, Gang-Jin,” ”Xie, Chi,” and ”Zhu, You” were identified, signifying their substantial impact within cohesive clusters of authors, emphasizing shared research interests through their co-cited works.

4.10.2 Countries

Figure  10 revealed 39 nations out of 67 that met the criteria, forming 6 clusters based on a minimum document threshold of 3 per country. These clusters provide insights into international collaborations and thematic concentrations among countries in scholarly publications. Standing out prominently are the People’s Republic of China and major contributors like England, Germany, and the USA, who demonstrate extensive involvement and collaborations in this domain, highlighting significant thematic concentrations among nations in scholarly publications.

4.10.3 Documents

In Figure  11 , 109 significant papers out of 434 met the criteria, forming 8 clusters based on a minimum citation threshold of 16 per document. These clusters highlight cohesive groups of highly cited papers, indicating thematic similarities and influential works. Here, influential authors with significant citation impact include ”Nayal,” ”Baryannis,” ”Ivanov,” ”Hosseini,” ”Modgil,” ”Zouari,” ”Wu,” and ”Hosseini,” suggesting their substantial contributions.

4.10.4 Organizations

In Figure  12 , 34 significant entities out of 769 met the dual criteria of a minimum document threshold of 5 and a minimum citation threshold of 10, forming 3 clusters. These clusters illustrate collaborative research efforts and the impact of organizations in the academic landscape. It shows institutions like ”Hong Kong Polytechnic University,” ”Neoma Business School,” and ”Indian Institute of Technology Delhi” as notable contributors to these cohesive groups.

4.10.5 Journals

For the bibliographic coupling analysis of sources, 42 significant publication sources out of 196 met the criteria, forming clusters based on a minimum document threshold of 3 per source, as shown in Figure  13 . These clusters reveal patterns in scholarly publishing, highlighting sources that frequently publish related content. Journals such as ”Journal of Computational and Applied Mathematics,” ”IEEE Transactions on Engineering Management,” ”Annals of Operations Research,” and ”International Journal of Production Research” frequently publish related content within their respective clusters.

4.11 Journal Publication Trends

Table  3 presents a comprehensive breakdown of published papers in various source titles from 2015 to 2023. These source titles encompass a range of journals and publications contributing to the field of SCRA. The table clearly shows the publication trends, indicating how many papers were published in each source title each year and their cumulative total. It provides valuable insights into the distribution of research output and the source titles that have been active in this area.

Through these figures and tables, our systematic literature review is empowered to offer an in-depth analysis of the SCRA landscape and provide valuable insights to both the academic and professional communities in this dynamic field.

5 AI Techniques for Supply Chain Risk Assessment

In recent years, the application of Machine Learning (ML) methods in Supply Chain Risk Assessment (SCRA) has garnered substantial attention due to its potential to enhance decision-making processes and mitigate operational risks. This review paper explores various ML techniques applied in SCRA, highlighting their methodologies, key findings, and contributions.

Han and Zhang, ( 2021 ) employed Factor Analysis and a Backpropagation Neural Network (BPNN) to identify high and extremely high-risk incentives in sustainable supply chains, offering valuable insights for risk assessment. Jianying et al., ( 2021 ) optimized a BPNN using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), enhancing its predictive accuracy, which can be instrumental in the fresh grape industry’s sustainable supply chain management.

Belhadi et al., 2021b utilized a hybrid Ensemble Machine Learning (EML) approach involving the Gama Test, Rotation Forest (RotF), and Logit Boosting (LB) algorithms, achieving excellent performance with the RotF-LB model, contributing to credit risk assessment in the context of Agriculture 4.0. Wu et al., 2022c improved credit risk prediction in agricultural SCF using a GA-BPNN Credit Risk Model, attaining a risk prediction accuracy above 0.92. Zhang et al., ( 2015 ) compared different credit risk assessment models for SCF, highlighting the superiority of the Support Vector Machine (SVM) model with an accuracy of 93.65%. Lei et al., ( 2023 ) demonstrated the effectiveness of the Support Vector Machine and Slime Mould Algorithm (SVM-SMA) model for financial risk assessment, with results showing Precision: 85.38%, F-score: 63%, and TNR: 72%.

Yin et al., ( 2022 ) introduced a Convolutional Neural Network (CNN) model for early warning of SCF risks, achieving an optimal accuracy at 200 iterations and a comprehensive accuracy of 94.7%. Chen et al., ( 2021 ) employed a Long Short-Term Memory (LSTM) network model to forecast oil import risk, achieving superior forecasting accuracy compared to other models, offering a more effective risk assessment solution for oil import scenarios. Janjua et al., ( 2023 ) developed a real-time social media data assessment tool for SCRM with the Bi-Directional Long Short-Term Memory Conditional Random Field (Bi-LSTM CRF) model, resulting in improved precision, recall, and F1 scores compared to baseline models. Yao et al., ( 2022 ) showcased the power of combining multiple ranking information and an ensemble feature selection method for enterprise credit risk assessment, obtaining the best prediction results. Aboutorab et al., ( 2022 ) demonstrated the application of Reinforcement Learning (RL) in proactive risk identification, achieving high accuracy in identifying various risks. Kosasih et al., ( 2022 ) presented a tensorization-based Neuro-Symbolic model that outperformed existing models and unveiled hidden supply chain risks.

Wang et al., 2022a introduced a novel cost-sensitive learning Random Forest model (CSL-RF) equipped with an imbalance sampling strategy to predict SMEs credit risk in SCF, achieving exceptional accuracy (97.%) and precision (1.00). Wang et al., ( 2021 ) addressed the small sample size issue in SME credit risk forecasting by proposing an Adaptive Heterogeneous Multiview Graph Learning (AH-MGL) method, which effectively improved interpretability and demonstrated robustness with an accuracy of 87.44%. Zhu et al., ( 2016 ) introduced the Random Subspace-Real AdaBoost (RS-RAB) method, showcasing its effectiveness in credit risk prediction, and emphasized the performance of ensemble methods against individual machine learning techniques, shedding light on their predictive capabilities. Bassiouni et al., ( 2023 ) presented a unique case study utilizing Deep Learning (DL) methodologies, such as Recurrent Neural Networks (RNN) and Temporal Convolution Neural Networks (TCN), to predict the exportability of shipments during the COVID-19 pandemic, significantly improving classification accuracy.

Zhu et al., ( 2019 ) introduced an enhanced hybrid Ensemble Machine Learning (EML) approach (RS-MultiBoosting) for forecasting SMEs’ credit risk, demonstrating its superiority, especially with small datasets. Athaudage et al., ( 2022 ) proposed a Random Forest (RF) regression model for crude oil price analysis during disease outbreaks, offering improved forecasting capabilities with high accuracy. Liu and Huang, ( 2020 ) combined AdaBoosting and SVM with Adaptive Mutation Particle Swarm Optimization (AM-PSO) and noise reduction techniques for risk assessment in SCF, resulting in higher credit assessment accuracy. Zhu et al., ( 2017 ) analyzed various EML methods, highlighting the effectiveness of the Random Subspace Boosting (RS-Boosting) approach in SME credit risk prediction.

Fu et al., ( 2022 ) explored the application of BPNN, SVM, and GA in risk assessment. The results highlighted the superiority of the BP-GA model in terms of classification accuracy, emphasizing the importance of innovative approaches to risk assessment. Zhang, ( 2022 ) introduced the Fuzzy Support Vector Machine Prediction Model (FSVM) for enterprise SCRA, demonstrating higher accuracy compared to traditional models, including SVM and Decision Trees (DT), and the neural network model Temporal-Spatial Fuzzy Neural Network (T-SFNN). This research addressed the need for company-focused SCRM solutions, especially in the context of macroeconomic environments and epidemics. Pan and Miao, ( 2023 ) presented a DL-based BPNN model for SCRA, showcasing exceptional generalization ability and highlighting its potential as an effective risk assessment method. Yang et al., ( 2021 ) introduced the Least Absolute Shrinkage and Selection Operator Logistic Regression (Lasso-Logistic) model, effectively overcoming multicollinearity and offering the best prediction accuracy and discrimination ability.

Zhang et al., ( 2022 ) introduced the DeepRisk model, emphasizing the importance of utilizing both static and dynamic data in credit risk prediction for SMEs in SCF. This model outperformed baseline methods in various credit risk prediction metrics, emphasizing the value of dynamic financing behavioral data. Wang, ( 2021 ) integrated Blockchain and Fuzzy Neural Network algorithms to study credit risk in SME financing within the SCF context. This approach significantly contributes to credit risk understanding and management within SCF. Li, ( 2022 ) applied BPNN and LR analysis to predict investment risk in SCM, demonstrating high prediction accuracy, which can be instrumental in improving SCM and the sustainable development of enterprises. Bodendorf et al., ( 2023 ) introduced a DL model that exhibited high predictive performance and enhanced interpretability for managers, answering the ”why” question. This approach represented an extension to SCRM research.

Liu et al., ( 2022 ) utilized a hybrid model chain, incorporating eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTENC), and Random Forest (RF), to identify and control credit risk in financial institutions. This approach contributed to developing integrated models that address interpretability and accuracy issues. Rao and Li, ( 2022 ) implemented various ML algorithms, including Logistic Regression (LR), Decision Trees (DT), and integrated Logistic Regression-Random Subspace (LR-RS) and Decision Trees-Random Subspace (DT-RS) methods to enhance risk assessment and behavior prediction in blockchain SCF. The Logistic Regression-Random Subspace (LR-RS) model demonstrated practicality and superiority in credit risk assessment. Zheng et al., ( 2023 ) introduced a Federated Learning (FL) method to address supply chain risk prediction. FL enabled supply chain members with smaller and imbalanced datasets to leverage collective information, enhancing predictive performance. It outperformed other algorithms, demonstrating the potential of collective learning in supply chain risk prediction.

Handfield et al., ( 2020 ) presented a method for predicting factory risks in Low-Cost Countries (LCCs) with a five-year planning horizon, enabling proactive risk assessment before sourcing decisions. This approach addressed the geographic-specific risks associated with supplier factories and informed sourcing allocation decisions. Wong et al., ( 2022 ) highlighted the significance of AI-driven risk management in enhancing Supply Chain Agility and Re-engineering Capabilities (RP). This study explored the potential of artificial intelligence in SME risk assessment and its impact on business continuity. Wang and Song, ( 2022 ) utilized the Interval Type 2 Fuzzy Neural Network (IT2FNN) method, effectively addressing periodic deception and exhibiting a high comprehensive accuracy rate. The research combined fuzzy logic systems and artificial neural networks to assess e-commerce credit risk.

Nezamoddini et al., ( 2020 ) introduced a novel GA integrated with an Artificial Neural Network (ANN) to optimize supply chain decision-making, emphasizing the robustness and profitability improvements the proposed technique offers. Zhang, ( 2022 ) presented an SVM based on a Fuzzy Model (FSVM) for enterprise SCRA. This method’s accuracy surpassed traditional models, contributing to the effectiveness of complex enterprise SCRA. Xia et al., ( 2023 ) explored ML methods for manufacturing SCF credit risk assessment. RF emerged as the best-performing model, and the research focused on analyzing the unique environment of Small and Medium-Sized Manufacturing Enterprises (SMMEs) in China. Zhao and Li, ( 2022 ) examined the use of SVM and BPNN algorithms for measuring risk in SCF, aiming to address the financing dilemma of SMEs.

Li and Fu, ( 2022 ) highlighted the superiority of the Principal Component Analysis-Genetic Algorithm-Support Vector Machine (PCA-GA-SVM) model for credit risk prediction in the specific context of SCF accounts receivable mode. The model exhibited better performance in terms of accuracy and error rates. Duan et al., ( 2021 ) employed a BPNN optimized by the GA model. This approach offered improved fitting effects, higher prediction precision, and faster convergence than conventional models. Zhang et al., ( 2021 ) introduced the Firefly Algorithm Support Vector Machine (FA-SVM) for financial credit risk assessment in supply chains. The FA-SVM enhanced classification efficiency and reduced error rates, making it more practical.

Luo et al., ( 2022 ) used SVM with improved optimization techniques, such as Dynamic Mutation Particle Swarm Optimization (DM-PSO), to optimize parameters. The integration of AdaBoost as a weak classifier resulted in an integrated model with superior performance. Dang et al., ( 2022 ) constructed a BPNN and incorporated blockchain technology for credit risk evaluation in SCF. The research demonstrated the effectiveness of deep learning and blockchain in supply chain financial prediction and management. Hosseini and Barker, ( 2016 ) proposed a Bayesian Network (BN) model to evaluate and select suppliers, considering criteria falling into primary, green, and resilience categories. This model quantified supplier resilience in absorptive, adaptive, and restorative capacities, addressing the importance of resilience in the supplier selection process. Gao et al., ( 2022 ) explored various ML models, including Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron-Long Short-Term Memory (MLP-LSTM), for commodity demand prediction. The research highlighted the effectiveness of the Autoregressive Mixture Density Network (AR-MDN) commodity prediction model and the Improved Particle Swarm Optimization (IPSO) algorithm.

Wei, ( 2022 ) introduced a Machine Learning-based Linear Regression Algorithm (ML-LRA) for credit risk assessment. This method efficiently reduced the risk of supplier credit risk assessment, contributing to the field of supply chain management. Deiva Ganesh and Kalpana, ( 2022 ) employed text mining to analyze live supply chain-related information from social media platforms, emphasizing the importance of real-time risk identification. Zhang et al., ( 2023 ) presented a Time-Decayed Long Short-Term Memory (TD-LSTM) algorithm for monitoring and interpolating missing data in irregular time series. This algorithmic model enhanced the predictability of datasets with missing data. Baryannis et al., 2019a proposed a Generic Data-Driven Risk Prediction Framework (GDDRPF) for SCRM. The framework emphasized the synergy between Artificial Intelligence (AI) and supply chain experts, considering the trade-off between interpretability and prediction performance.

Collectively, these papers contribute to the evolving field of supply chain risk assessment and management, leveraging various machine learning, optimization, and data-driven approaches to enhance prediction, decision-making, and resilience within supply chains.

5.1 Used ML Models and Key Findings

Table 4 summarizes various research methods or models used in risk prediction and analysis, along with the key findings and associated metrics from different research papers. Each row in the table represents a research paper’s first author, the research method or model used, and the specific findings and metrics achieved. These methods and findings are crucial in understanding risk prediction in various domains.

5.2 Clustering of the ML Models

Table  5 presents a clustering of ML models utilized in SCRA, categorizing them into diverse methodological clusters. It offers a comprehensive view of various approaches employed, including ensemble learning, deep learning, time series models, fuzzy logic models, reinforcement learning, dimensionality reduction, genetic algorithms, Bayesian models, and other innovative approaches used in this domain.

6 Managerial Implications

The findings of this study have significant implications for managerial strategies aimed at enhancing SCRM. Firstly, it underscores the pivotal role of ML and AI in revolutionizing SCRA. Managers are encouraged to explore the adoption of advanced ML models, such as Random Forest, XGBoost, and hybrid approaches, as they have demonstrated substantial potential in improving risk assessment precision. These models can significantly bolster the accuracy of risk evaluation and enhance the overall effectiveness of risk management strategies.

Moreover, the research emphasizes the importance of adaptability and flexibility in the post-COVID era, highlighting the need for proactive supply chain strategies. Managers should prioritize the development of contingency plans that are resilient to potential future disruptions, ensuring the continuity of operations and minimizing the impact of unforeseen events.

The study also underscores the effectiveness of ensemble models, such as Random Forest and XGBoost, along with hybrid approaches in risk assessment. These models can potentially enhance risk prediction precision and should be considered by managers seeking to fortify their SCRM. Time series forecasting techniques, including ARIMA and LSTM, emerge as pivotal in anticipating future risks. Organizations are encouraged to invest in predictive models grounded in historical data, providing them with the foresight required to mitigate supply chain disruptions effectively.

The significance of XAI and ML models cannot be overstated. Ensuring that models offer clear and interpretable results is essential, as this aids decision-makers in comprehending the risk assessment process and its outcomes. The study also emphasizes the importance of real-time data mining and incorporating unconventional data sources, such as social media platforms like Twitter, as essential elements of comprehensive risk identification. Managers must stay vigilant, continually updating their understanding of real-time information.

Blockchain technology emerges as a potent tool for enhancing transparency and traceability within the supply chain. Organizations considering blockchain solutions should evaluate their applications for risk mitigation, ultimately enhancing SCR. SMEs warrant tailored strategies for credit risk assessment, accounting for their unique operational challenges. Implementing strategies aligned with the specific needs and constraints of SMEs is vital. In cases where data privacy is a concern, the adoption of federated learning is recommended. This approach safeguards sensitive information while still enabling collaborative risk prediction. Ensuring data security and privacy compliance should be a top priority.

Hybrid risk assessment methodologies, which integrate various ML models and techniques, offer a holistic view of SCRA. Managers should explore these comprehensive approaches to build robust risk management strategies. Benchmarking different ML models is crucial for identifying the most effective ones for specific risk scenarios. The study underscores the significance of continuous performance evaluation and improvement, ensuring that risk assessment strategies evolve with changing circumstances. Integrating ML-based risk assessment into existing supply chain workflows and decision-making processes is pivotal to ensure seamless implementation. Managers should explore ways to align these technologies with their current operational strategies. Recognizing the importance of employee training in data analysis, ML, and AI is fundamental. Managers should invest in skill development to empower their supply chain professionals to harness these technologies effectively. Collaboration and knowledge sharing within organizations and across industries are encouraged to collectively build robust risk management strategies. Such partnerships have the potential to enhance risk resilience significantly.

Lastly, understanding and adhering to data protection and privacy regulations is critical when deploying AI and ML for risk assessment. Managers should ensure that their risk management practices are fully aligned with the legal requirements to avoid any compliance issues. The study offers a comprehensive roadmap for organizations aiming to fortify their SCRM strategies in an ever-evolving landscape.

7 Challenges and Limitations

Integrating AI in SCRA brings forth several challenges and limitations that researchers and practitioners must address. These challenges encompass the following:

Data Availability and Quality: AI models in SCRA rely heavily on data. However, ensuring the availability and quality of data remains a persistent challenge. Supply chain data often comes from various sources, which can be fragmented, incomplete, or outdated. Researchers must explore ways to consolidate, cleanse, and augment data to enhance the effectiveness of AI models.

Interpretability and Explainability: Many AI techniques, such as deep learning and neural networks, are often considered as ”black-box” models. This lack of interpretability and explainability is a significant limitation. In SCRA, stakeholders require insights into why a model makes a particular prediction or decision. Developing AI models that are not only accurate but also interpretable is crucial for their adoption in real-world supply chain management.

Dynamic and Evolving Risk Factors: The supply chain environment is dynamic and continuously evolving. New risk factors emerge, and existing ones change over time. AI models may struggle to adapt to these dynamic conditions. Researchers need to explore techniques for real-time risk assessment and adaptive models that can respond to changing risk profiles.

Integration with Existing Systems: Integrating AI solutions into existing SCM systems can be challenging. Organizations often rely on legacy systems, and ensuring compatibility and seamless integration can pose hurdles. Research in this area should focus on creating modular AI solutions that can be integrated into diverse systems with minimal disruption.

8 Future Research Directions

In light of the challenges and limitations, the future research directions in the field of AI-based supply chain risk assessment are multi-faceted and hold the potential to advance the state-of-the-art:

AI Model Explainability: Emphasize the development of AI models that deliver accurate predictions and offer comprehensive transparency and interpretability. Techniques like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) serve as pivotal approaches to enhance the interpretability of AI models. By employing these methods, the goal is to demystify the decision-making process of AI models, enabling stakeholders to comprehend the rationale behind specific predictions or decisions. This transparency enhances trust and facilitates more effective integration of AI-driven insights into supply chain risk management strategies.

Hybrid AI Approaches: Combining multiple AI techniques, such as machine learning and expert systems, can enhance the robustness of supply chain risk assessment. Future research should explore hybrid AI models that leverage the strengths of different approaches to improve prediction accuracy and reliability.

Data Quality and Accessibility: Addressing data quality challenges involves multifaceted strategies. Developing robust methods for data cleansing, augmentation, and ensuring high-quality data remains a critical focus. Beyond that, initiatives should be aimed at democratizing access to standardized supply chain data repositories. Creating and maintaining such repositories enables stakeholders across the supply chain spectrum to access reliable and consistent data. This increased accessibility fosters better-informed decision-making processes and promotes using standardized data sets for improved AI model training and validation.

Real-time Risk Assessment: Investigating AI models capable of real-time risk assessment will be pivotal. These models should adapt to emerging risks and provide timely alerts or recommendations for risk mitigation.

Integration Frameworks: Research on creating integration frameworks for AI-based supply chain risk assessment is essential. These frameworks should facilitate easy integration into existing supply chain management systems, ensuring a smooth transition to AI-driven risk assessment.

Cross-Domain Application: Expanding the application of AI in risk assessment to various domains within the supply chain, including logistics, procurement, and production, offers opportunities for comprehensive risk management.

Adaptation to Pandemic Dynamics (e.g., COVID-19): Investigate AI models capable of adapting to pandemic-induced disruptions within supply chains. Focus on understanding the unique dynamics and challenges posed by events like COVID-19, aiming to develop AI-driven strategies that enable resilience and agility in such crisis scenarios.

Ethical and Legal Considerations: As AI plays an increasingly significant role in decision-making, researchers should also address ethical and legal considerations. This includes issues related to data privacy, bias, and fairness in AI models.

By addressing these challenges and pursuing these research directions, the field of AI-based supply chain risk assessment can make significant strides toward more effective and reliable risk management in an ever-evolving global supply chain landscape.

9 Conclusions

In this extensive review, we delved into the supply chain risk assessment domain and examined the complex challenges that arise when integrating artificial intelligence, especially machine learning. Our thorough analysis encompassed a substantial pool of 1,717 SCRA papers, ultimately narrowing our focus to a refined subset of 48 papers. This endeavor has yielded significant insights and key findings, making substantial contributions to the SCRA literature.

Our primary contributions can be summarized by addressing the four key research questions:

Our review thoroughly analyzes the existing state of research concerning the application of AI and ML techniques in SCRM. We have meticulously assessed a substantial body of literature to extract meaningful insights, specifically 48 selected articles.

Through our analysis, we have identified the AI and ML techniques that are most commonly utilized in the domain of SCRA. This adds clarity to the methodologies employed and underscores the trends shaping the field.

Our review highlights the key findings and trends prevalent in the literature, shedding light on the progress, developments, and major areas of focus within the realm of AI and ML in SCRM.

We have identified critical research gaps and proposed future directions to guide further explorations in this field, ensuring the continued evolution of SCRA methods.

Despite these contributions, our research has some limitations. Our data primarily originated from Google Scholar and Web of Science rather than Scopus, potentially introducing biases. Our focus on papers published between 2014 and 2023 introduces a limitation, as it may omit earlier relevant research that could provide valuable historical context. Additionally, our restriction to English-language papers may have omitted valuable non-English research.

Looking forward, several promising avenues for future research emerge in the domain of SCRA using AI. These directions encompass operationalizing interconnectedness, transformability, and sharing within SCRA frameworks, investigating evolving ICT roles in prediction and response, revisiting the cost implications of resilience enhancement, and exploring emerging technologies like blockchain. These endeavors are expected to enhance risk assessment effectiveness in the post-COVID era. Reviewers should adopt a comprehensive approach to gain deeper insights into this field by expanding their search to diverse databases, including non-English and grey literature sources, using snowballing techniques, collaborating with experts from related disciplines, leveraging ML tools for data analysis, and staying up-to-date with the latest research. Combining various search methods and expert opinions, such a multidisciplinary strategy is essential for uncovering valuable insights and emerging trends in this dynamic and critical field.

Our SLR, driven by these four primary research questions, has contributed valuable insights into the AI/ML application in SCRA, identified common techniques, outlined key findings and trends, and proposed essential research directions. This work serves as a guide for both researchers and practitioners, facilitating advancements in SCRM.

• Aboutorab et al., (2022) Aboutorab, H., Hussain, O. K., Saberi, M., and Hussain, F. K. (2022). A reinforcement learning-based framework for disruption risk identification in supply chains. Future Generation Computer Systems , 126:110–122.
• Agca et al., (2022) Agca, S., Babich, V., Birge, J. R., and Wu, J. (2022). Credit Shock Propagation Along Supply Chains: Evidence from the CDS Market. Management Science , 68(9):6506–6538. Publisher: INFORMS.
• Araz et al., (2020) Araz, O. M., Choi, T.-M., Olson, D. L., and Salman, F. S. (2020). Data Analytics for Operational Risk Management. Decision Sciences , 51(6):1316–1319. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/deci.12443.
• Athaudage et al., (2022) Athaudage, G. N., Perera, H. N., Sugathadasa, P. R. S., De Silva, M. M., and Herath, O. K. (2022). Modelling the impact of disease outbreaks on the international crude oil supply chain using Random Forest regression. International Journal of Energy Sector Management , ahead-of-print(ahead-of-print).
• (5) Baryannis, G., Dani, S., and Antoniou, G. (2019a). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems , 101:993–1004.
• (6) Baryannis, G., Validi, S., Dani, S., and Antoniou, G. (2019b). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research , 57(7):2179–2202. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2018.1530476.
• Bassiouni et al., (2023) Bassiouni, M. M., Chakrabortty, R. K., Hussain, O. K., and Rahman, H. F. (2023). Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions. Expert Systems with Applications , 211:118604.
• (8) Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., and Venkatesh, M. (2021a). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change , 163:120447.
• (9) Belhadi, A., Kamble, S. S., Mani, V., Benkhati, I., and Touriki, F. E. (2021b). An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance. Annals of Operations Research .
• Bode et al., (2011) Bode, C., Wagner, S. M., Petersen, K. J., and Ellram, L. M. (2011). Understanding Responses to Supply Chain Disruptions: Insights from Information Processing and Resource Dependence Perspectives. Academy of Management Journal , 54(4):833–856. Publisher: Academy of Management.
• Bodendorf et al., (2023) Bodendorf, F., Sauter, M., and Franke, J. (2023). A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning. International Journal of Production Economics , 256:108708.
• Chen et al., (2021) Chen, S., Song, Y., Ding, Y., Zhang, M., and Nie, R. (2021). Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory. Energy , 215:119152.
• Craighead et al., (2020) Craighead, C. W., Ketchen Jr., D. J., and Darby, J. L. (2020). Pandemics and Supply Chain Management Research: Toward a Theoretical Toolbox*. Decision Sciences , 51(4):838–866. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/deci.12468.
• Dang et al., (2022) Dang, C., Wang, F., Yang, Z., Zhang, H., and Qian, Y. (2022). Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model. Operations Management Research , 15(3):662–675.
• Deiva Ganesh and Kalpana, (2022) Deiva Ganesh, A. and Kalpana, P. (2022). Supply chain risk identification: a real-time data-mining approach. Industrial Management & Data Systems , 122(5):1333–1354. Publisher: Emerald Publishing Limited.
• Denyer and Tranfield, (2009) Denyer, D. and Tranfield, D. (2009). Producing a systematic review. In The Sage handbook of organizational research methods , pages 671–689. Sage Publications Ltd, Thousand Oaks, CA.
• Duan et al., (2021) Duan, Y., Mu, C., Yang, M., Deng, Z., Chin, T., Zhou, L., and Fang, Q. (2021). Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms. International Journal of Production Economics , 242:108293.
• Fu et al., (2022) Fu, W., Zhang, H., and Huang, F. (2022). Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM. PLOS ONE , 17(1):e0262222. Publisher: Public Library of Science.
• Gao et al., (2022) Gao, Q., Xu, H., and Li, A. (2022). The analysis of commodity demand predication in supply chain network based on particle swarm optimization algorithm. Journal of Computational and Applied Mathematics , 400:113760.
• Govindan et al., (2020) Govindan, K., Mina, H., and Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review , 138:101967.
• Guo et al., (2021) Guo, Y., Fu, Y., Hao, F., Zhang, X., Wu, W., Jin, X., Robin Bryant, C., and Senthilnath, J. (2021). Integrated phenology and climate in rice yields prediction using machine learning methods. Ecological Indicators , 120:106935.
• Han and Zhang, (2021) Han, C. and Zhang, Q. (2021). Optimization of supply chain efficiency management based on machine learning and neural network. Neural Computing and Applications , 33(5):1419–1433.
• Handfield et al., (2020) Handfield, R., Sun, H., and Rothenberg, L. (2020). Assessing supply chain risk for apparel production in low cost countries using newsfeed analysis. Supply Chain Management: An International Journal , 25(6):803–821. Publisher: Emerald Publishing Limited.
• Hosseini and Barker, (2016) Hosseini, S. and Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics , 180:68–87.
• Ivanov, (2020) Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review , 136:101922.
• Janiesch et al., (2021) Janiesch, C., Zschech, P., and Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets , 31(3):685–695.
• Janjua et al., (2023) Janjua, N. K., Nawaz, F., and Prior, D. D. (2023). A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterprise Information Systems , 17(4):1959652. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/17517575.2021.1959652.
• Jianying et al., (2021) Jianying, F., Bianyu, Y., Xin, L., Dong, T., and Weisong, M. (2021). Evaluation on risks of sustainable supply chain based on optimized BP neural networks in fresh grape industry. Computers and Electronics in Agriculture , 183:105988.
• Katsaliaki et al., (2022) Katsaliaki, K., Galetsi, P., and Kumar, S. (2022). Supply chain disruptions and resilience: a major review and future research agenda. Annals of Operations Research , 319(1):965–1002.
• Kosasih et al., (2022) Kosasih, E. E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N., and Brintrup, A. (2022). Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research , 0(0):1–17. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2022.2100841.
• Kwak et al., (2018) Kwak, D.-W., Seo, Y.-J., and Mason, R. (2018). Investigating the relationship between supply chain innovation, risk management capabilities and competitive advantage in global supply chains. International Journal of Operations & Production Management , 38(1):2–21. Publisher: Emerald Publishing Limited.
• Lalmi et al., (2021) Lalmi, A., Fernandes, G., and Souad, S. B. (2021). A conceptual hybrid project management model for construction projects. Procedia Computer Science , 181:921–930.
• Lei et al., (2023) Lei, Y., Qiaoming, H., and Tong, Z. (2023). Research on Supply Chain Financial Risk Prevention Based on Machine Learning. Computational Intelligence and Neuroscience , 2023:e6531154. Publisher: Hindawi.
• Li et al., (2023) Li, D., Zhi, B., Schoenherr, T., and Wang, X. (2023). Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis. IISE Transactions , 55(12):1256–1276. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/24725854.2023.2176951.
• Li, (2022) Li, L. (2022). Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning. Wireless Communications and Mobile Computing , 2022:e4340286. Publisher: Hindawi.
• Li and Fu, (2022) Li, M. and Fu, Y. (2022). Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model. Sustainability , 14(24):16376. Number: 24 Publisher: Multidisciplinary Digital Publishing Institute.
• Liu et al., (2022) Liu, J., Liu, S., Li, J., and Li, J. (2022). Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain. International Journal of Intelligent Systems , 37(11):8790–8813. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22968.
• Liu and Huang, (2020) Liu, Y. and Huang, L. (2020). Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks , 16(1):1550147720903631. Publisher: SAGE Publications.
• Liu et al., (2019) Liu, Z., Gao, R., Zhou, C., and Ma, N. (2019). Two-period pricing and strategy choice for a supply chain with dual uncertain information under different profit risk levels. Computers & Industrial Engineering , 136:173–186.
• Luo et al., (2022) Luo, S., Xing, M., and Zhao, J. (2022). Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment. Scientific Programming , 2022:e4194576. Publisher: Hindawi.
• Mohanty et al., (2021) Mohanty, D. K., Parida, A. K., and Khuntia, S. S. (2021). Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine. Applied Soft Computing , 99:106898.
• Nayal et al., (2021) Nayal, K., Raut, R. D., Queiroz, M. M., Yadav, V. S., and Narkhede, B. E. (2021). Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective. The International Journal of Logistics Management , 34(2):304–335. Publisher: Emerald Publishing Limited.
• Naz et al., (2022) Naz, F., Kumar, A., Majumdar, A., and Agrawal, R. (2022). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research , 15(1):378–398.
• Nezamoddini et al., (2020) Nezamoddini, N., Gholami, A., and Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics , 225:107569.
• Ni et al., (2020) Ni, D., Xiao, Z., and Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics , 11(7):1463–1482.
• Nimmy et al., (2022) Nimmy, S. F., Hussain, O. K., Chakrabortty, R. K., Hussain, F. K., and Saberi, M. (2022). Explainability in supply chain operational risk management: A systematic literature review. Knowledge-Based Systems , 235:107587.
• Pan and Miao, (2023) Pan, W. and Miao, L. (2023). Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach. The Journal of Supercomputing , 79(4):3878–3901.
• Punia et al., (2020) Punia, S., Singh, S. P., and Madaan, J. K. (2020). From predictive to prescriptive analytics: A data-driven multi-item newsvendor model. Decision Support Systems , 136:113340.
• Rao and Li, (2022) Rao, Q. and Li, W. (2022). Risk Evaluation and Forecast Behavior Analysis of Supply Chain Financing Based on Blockchain. Wireless Communications and Mobile Computing , 2022:e7668474. Publisher: Hindawi.
• Roukny et al., (2018) Roukny, T., Battiston, S., and Stiglitz, J. E. (2018). Interconnectedness as a source of uncertainty in systemic risk. Journal of Financial Stability , 35:93–106.
• Schroeder and Lodemann, (2021) Schroeder, M. and Lodemann, S. (2021). A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management. Logistics , 5(3):62. Number: 3 Publisher: Multidisciplinary Digital Publishing Institute.
• Shahed et al., (2021) Shahed, K. S., Azeem, A., Ali, S. M., and Moktadir, M. A. (2021). A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk. Environmental Science and Pollution Research .
• Sheng et al., (2021) Sheng, J., Amankwah-Amoah, J., Khan, Z., and Wang, X. (2021). COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions. British Journal of Management , 32(4):1164–1183. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-8551.12441.
• Shi et al., (2022) Shi, S., Tse, R., Luo, W., D’Addona, S., and Pau, G. (2022). Machine learning-driven credit risk: a systemic review. Neural Computing and Applications , 34(17):14327–14339.
• Wang et al., (2021) Wang, C., Yu, F., Zhang, Z., and Zhang, J. (2021). Multiview Graph Learning for Small- and Medium-Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance. Complexity , 2021:e6670873. Publisher: Hindawi.
• (56) Wang, L., Jia, F., Chen, L., and Xu, Q. (2022a). Forecasting smes’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques. Annals of Operations Research , pages 1–33.
• (57) Wang, L., Jia, F., Chen, L., and Xu, Q. (2022b). Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques. Annals of Operations Research .
• Wang and Song, (2022) Wang, L. and Song, H. (2022). E-Commerce Credit Risk Assessment Based on Fuzzy Neural Network. Computational Intelligence and Neuroscience , 2022:e3088915. Publisher: Hindawi.
• Wang, (2021) Wang, Y. (2021). Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks. Wireless Communications and Mobile Computing , 2021:e5565980. Publisher: Hindawi.
• Wei, (2022) Wei, Y. (2022). A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management. International Transactions on Electrical Energy Systems , 2022:e4766597. Publisher: Hindawi.
• Wong et al., (2020) Wong, C. W. Y., Lirn, T.-C., Yang, C.-C., and Shang, K.-C. (2020). Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization. International Journal of Production Economics , 226:107610.
• Wong et al., (2022) Wong, L.-W., Tan, G. W.-H., Ooi, K.-B., Lin, B., and Dwivedi, Y. K. (2022). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research , 0(0):1–21. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2022.2063089.
• (63) Wu, J., Zhang, Z., and Zhou, S. X. (2022a). Credit rating prediction through supply chains: A machine learning approach. Production and Operations Management , 31(4):1613–1629.
• (64) Wu, J., Zhang, Z., and Zhou, S. X. (2022b). Credit Rating Prediction Through Supply Chains: A Machine Learning Approach. Production and Operations Management , 31(4):1613–1629. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/poms.13634.
• (65) Wu, Y., Li, X., Liu, Q., and Tong, G. (2022c). The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network. Computational Economics , 60(4):1269–1292.
• Xia et al., (2023) Xia, Y., Xu, T., Wei, M.-X., Wei, Z.-K., and Tang, L.-J. (2023). Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Sustainability , 15(2):1087. Number: 2 Publisher: Multidisciplinary Digital Publishing Institute.
• Xu and Jackson, (2019) Xu, C. and Jackson, S. A. (2019). Machine learning and complex biological data. Genome Biology , 20(1):76.
• Xu et al., (2020) Xu, S., Zhang, X., Feng, L., and Yang, W. (2020). Disruption risks in supply chain management: a literature review based on bibliometric analysis. International Journal of Production Research , 58(11):3508–3526. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2020.1717011.
• Yang et al., (2023) Yang, M., Lim, M. K., Qu, Y., Ni, D., and Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering , 175:108859.
• Yang et al., (2021) Yang, Y., Chu, X., Pang, R., Liu, F., and Yang, P. (2021). Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China. Sustainability , 13(10):5714. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute.
• Yao et al., (2022) Yao, G., Hu, X., and Wang, G. (2022). A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain. Expert Systems with Applications , 200:117002.
• Yin et al., (2022) Yin, L.-L., Qin, Y.-W., Hou, Y., and Ren, Z.-J. (2022). A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning. Computational Intelligence and Neuroscience , 2022:e7825597. Publisher: Hindawi.
• Zhang et al., (2020) Zhang, F., Wu, X., Tang, C. S., Feng, T., and Dai, Y. (2020). Evolution of Operations Management Research: from Managing Flows to Building Capabilities. Production and Operations Management , 29(10):2219–2229. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/poms.13231.
• Zhang et al., (2021) Zhang, H., Shi, Y., Yang, X., and Zhou, R. (2021). A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Research in International Business and Finance , 58:101482.
• Zhang et al., (2015) Zhang, L., Hu, H., and Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation , 1(1):14.
• Zhang et al., (2023) Zhang, W., Lim, M. K., Yang, M., Li, X., and Ni, D. (2023). Using deep learning to interpolate the missing data in time-series for credit risks along supply chain. Industrial Management & Data Systems , 123(5):1401–1417. Publisher: Emerald Publishing Limited.
• Zhang et al., (2022) Zhang, W., Yan, S., Li, J., Tian, X., and Yoshida, T. (2022). Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data. Transportation Research Part E: Logistics and Transportation Review , 158:102611.
• Zhang, (2022) Zhang, X. (2022). Enterprise Supply Chain Risk Assessment Based on the Support Vector Machine Algorithm and Fuzzy Model. Security and Communication Networks , 2022:e3692628. Publisher: Hindawi.
• Zhao and Li, (2022) Zhao, J. and Li, B. (2022). Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network. Neural Computing and Applications , 34(15):12467–12478.
• Zheng et al., (2023) Zheng, G., Kong, L., and Brintrup, A. (2023). Federated machine learning for privacy preserving, collective supply chain risk prediction. International Journal of Production Research , 0(0):1–18. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2022.2164628.
• Zhu et al., (2016) Zhu, Y., Xie, C., Wang, G.-J., and Yan, X.-G. (2016). Predicting China’s SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Entropy , 18(5):195. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute.
• Zhu et al., (2017) Zhu, Y., Xie, C., Wang, G.-J., and Yan, X.-G. (2017). Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Computing and Applications , 28(1):41–50.
• Zhu et al., (2019) Zhu, Y., Zhou, L., Xie, C., Wang, G.-J., and Nguyen, T. V. (2019). Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics , 211:22–33.

Appendix A Co-authorship Analysis Clusters

Appendix B Co-citation Analysis Clusters

B.1 cited references.

B.2 Authors

Appendix C Bibliographic Coupling Clusters

C.1 authors.

C.2 Countries

C.3 Documents

C.4 Organizations

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

Efpraxia d. zamani.

1 Information School, The University of Sheffield, Sheffield, UK

2 Business Information Systems, NUI Galway, Galway, Ireland

Samrat Gupta

3 Information Systems Area, Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat India

Denis Dennehy

4 School of Management, Swansea University, Swansea, UK

Associated Data

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Introduction

Exogenous shocks transcend our previous experiences and have significant impacts, altering the competitive landscape within which businesses operate (Zamani et al., 2022 ). The Covid-19 pandemic has been characterised as such a shock (Wenzel et al., 2021 ) and since its outbreak, it has resulted in significant loss of life. Within the world of business, we have been witnessing a number of negative impacts and business failures, such as layoffs, closures and bankruptcies (Amankwah-Amoah et al., 2020 ). To a large extent, these outcomes were the result of adopting required social distancing measures to minimise the spread of the virus, which negatively impacted the sustainability and profitability of several businesses.

Within the context of operations and supply chains in particular, an important implication of this exogenous shock relates to great uncertainties. In many cases, the latter have been observed due to the pervasive dissemination of false news that has caused further disruption for businesses and everyday life (Verma & Gustafsson, 2020 ), to the tune of leading to what has been termed as an ‘infodemic’, spreading through online and mainstream media (Zarocostas, 2020 ). This infodemic has impacted consumer behaviour, whereby consumers turned to panic buying and hoarding of medical, cleaning and non-perishable supplies, motivated by fear of potential product unavailability (Zwanka & Buff, 2021 ). Not unexpectedly, this abrupt change in consumer behaviour has resulted in turn in supply chain disruptions, too (Kirk & Rifkin, 2020 ), with businesses trying to cope with and forecast demand so as to adjust their supply chain and operations.

From an operations perspective, such disruptions are often considered through the lens of risk management, because ultimately, disruptions are seen as potential risks that need to be anticipated and mitigated against. More precisely, as far as the impacts stemming from such misinformation and media hype are concerned, supply chain professionals are required to manage the risk of potential stock-outs versus stock holding stocks (Jüttner et al., 2003 ). Indeed, recent research has shown that the bullwhip effect created by Covid-19 misinformation resulted quite quickly to inventory excess, stockpiling and critical issues with managing stocks (Kapoor et al., 2021 ).

To address such challenges, businesses traditionally develop business continuity plans alongside risk management strategies to mitigate against disruptions (Azadegan et al., 2020 ). Some typical tangible strategies that safeguard against such risks are the implementation of vendor-managed inventory contractual agreements (Lee, 2016 ) and the configuration of leagile supply chains that increase the performance of the firm in light of uncertainties (Fadaki et al., 2020 ). However, studies have shown that emerging technologies, such as Artificial Intelligence (AI)and Business Data Analytics (BDA) among others, are indispensable towards providing business continuity, especially during exogenous shocks (Papadopoulos et al., 2020 ). Supply chains are today enhanced by sensors and actuators, such as RFIDs, GPS and POS, tags and other smart devices, all of which (continuously) send and receive data (Fosso Wamba et al., 2018 ), thus making the Internet of Things (IoT) a potential avenue for accurate predictions. However, it is technologies like BDA and AI that make such data streams useful and actionable for risk mitigation and for overcoming the challenges of misinformation: insights, for example, from BDA can support the incremental improvement and transformation of the operation model, through accurate and on-time insights regarding the supply network (Roden et al., 2017 ), while AI can be leveraged for developing proactive strategies for predicting the likelihood of risks occurring and their impact (Baryannis et al., 2019 ). As such, emerging technologies like BDA and AI can play a pivotal role in mitigating the negative impacts and support decision-makers in forming appropriate decisions and actions to tackle challenging situations.

To date, there is an ever growing interest with regards to the use and application of AI and BDA for risk management and developing and maintaining resilience in supply chains (e.g., Baryannis et al., 2019 ; Modgil, Singh, et al., 2021; Sanders 2016 ). Despite this interest, however, there are still areas less understood. A recent major review of supply chain resilience focused on research conducted over the past 20 years, detailing the types of disruptions along with their impact on the supply chain and recovery strategies for mitigating these, while technology has been examined at a rather abstract level (Katsaliaki et al., 2021 ). Other studies have focused on identifying and classifying the different AI techniques used for risk management (Baryannis et al., 2019 ; Hamdi et al., 2018 ), and on evaluating different such techniques as part of supply chain resilience (Belhadi, Kamble, Fosso Wamba, et al., 2021). In both cases, scholars seem less focused on how AI contributes towards resilience and along the different phases of risk management (readiness, response, recovery, adaptability). Others have found that AI supports the development of dynamic capabilities, which can in turn facilitate resilience within the firm as far as its supply chain is concerned (Modgil, Singh, et al., 2021). However, such a perspective does not necessarily explain what the exact role of AI and BDA is in supporting resilience beyond supporting organisational dynamic capabilities. Therefore, further research is needed to explore the contribution of technologies such as AI and BDA for building and maintaining resilience in the supply chain. We posit that there is still scope for consolidating existing findings regarding the benefits of BDA and AI in the supply chain resilience (SCR) literature and further exploring the phases of SCR that these technologies can improve in light of significant misinformation and disruption.

The overarching research question that drives this research is: “How do BDA and AI contribute towards supply chain resilience?” To address this question, we specifically examine: “what is the current state of AI and BDA in the SC literature on resilience”, “what phases of SCR (readiness, response, recovery, adaptability) has BDA and AI been shown to improve” and finally, “what are the claimed benefits of BDA and AI in SCR literature”. We adopt a systematic literature review approach that first helps us explore uses and applications of BDA and AI over the last ten years to provide a holistic understanding of the field. Second, this approach helps us explore in more detail what are the claimed benefits of BDA and AI in SCR. By synthesising the findings of prior studies, we identify the exact functions of these technologies that contribute towards resilience in supply chains. In doing so, the paper then focuses on the current challenges that either prohibit or inhibit (externally or internally, respectively) the application and exploitation of BDA and AI for overcoming risks and the misinformation impacts.

Resilience in supply chains

Supply chains today operate within an increasingly uncertain and competitive environment, where disruptions can have a significant impact on business performance (Azadegan et al., 2020 ). Such disruptions can be the result of accidents (Stecke & Kumar, 2009 ), natural but also man-made disasters (Elluru et al., 2019 ), including events as for example the 2008 global financial crisis, the UK’s withdrawal from the European Union (Brexit) (Belhadi, Kamble, Fosso Wamba, et al., 2021), loss of critical suppliers (Ponomarov & Holcomb, 2009 ), and many others.

Supply chain systems during the Covid-19 pandemic have been particularly susceptible to disruptions because of the volatile demand, stemming from incomplete and often misleading information circulated, that resulted in misinformation with regards to “procurement, capacity allocation, contracting, scheduling, postponement and demand forecasting” (Gunessee & Subramanian, 2020 , p. 1202). Such misinformation has resulted to negative implications regarding consumer behaviour, and triggered in turn substantial and often difficult to handle fluctuations in demand (Ivanov, 2020 ), thus resulting in increased uncertainty.

To address such disruptions, the literature has highlighted the need to consider resilience of supply chains and to further delve on this concept, rather than restricting the discourse to solely risks (Gunessee & Subramanian, 2020 ). Resilience in general reflects a company’s ability to return to a business-as-usual state with regards to production and services following a major disruption (Rezapour et al., 2017 ). Specifically for supply chains, resilience describes the readiness of an organisation or business to address risks, uncertainty, and generally disruptions that may originate from customers, suppliers or other business processes and supply chain integration mechanisms used (Purvis et al., 2016 ).

Because disruptions can have significant repercussions for both revenues and costs (Ponomarov & Holcomb, 2009 ) and may lead to reputational damages(Elluru et al., 2019 ), to date, the literature has highlighted that overcoming disruptions is of critical importance for businesses. As such, within the operations and supply chain literatures, the concept of resilience is well integrated, as part of preparedness strategies, adopted by businesses for addressing disruptions (Pettit et al., 2019 ). Resilience can be defined as a system’s ability to return to its normal operating capacity within some identified bounds (Ioannidis et al., 2019 ), or to be more specific to supply chain systems, as the supply chain’s adaptive capacity to deal with disruption and quickly resume its previous performance (Belhadi, Kamble, Fosso Wamba, et al., 2021). Ponomarov and Holcomb have formally defined supply chain resilience as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruption and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structures and function” (Ponomarov & Holcomb, 2009 , p. 131).

Maintaining supply chain resilience allows businesses to ensure a continuous supply for their products (or services) to customers, despite turbulence in the environment. There are different approaches to ensuring supply chain resilience and these can be proactive, reactive or a combination of both, with the view to mitigate potential risks emerging in the environment (Lohmer et al., 2020 ). Existing scholarship suggests that, irrespective of the exact strategy adopted, disruptions in supply chain systems necessitate monitoring and controlling the environment on the one hand, and on the other hand being responsive and flexible in resource orchestration and reconfiguration as and when needed (Ralston & Blackhurst, 2020 ). To achieve this, scholars indicate that what is needed is superior information processing capabilities (Belhadi, Kamble, Fosso Wamba, et al., 2021), particularly because knowledge and information exchange among supply chain partners is considered conducive to risk reduction (Brandon-Jones et al., 2014 ). Indeed, information sharing can minimise ambiguities along the supply chain and increase visibility and performance (Wong et al., 2021 ), especially at times when these are put to the test. Wong et al., ( 2021 ) indicate that having access to accurate information before, during and after disruptions is of paramount importance, and that this can be achieved via real-time information exchange amongst supply chain partners. Such information exchange can support timely decision making and enhance the efficiency of the supply chain system (Li & Lin, 2006 ).

Belhadi et al., ( 2021a , b , c ) note that there has been an increasing interest in building supply chain resilience via technological means, such as the exploitation of advanced technologies, like BDA and AI. Indeed, such emerging technologies can significantly support building supply chain resilience through the lens of accurate and timely data, information, and knowledge exchange among partners. For example, predictive analytics can support the design of disaster-resilient supply chains because it facilitates forecasting, decision making and speedier return to business-as-usual states (Hazen et al., 2018 ). Similarly, AI can be deployed, often as part of an overall Industry 4.0 approach, for supporting adaptation and evolution of smart information systems along the supply chain and as part of operations management (Ralston & Blackhurst, 2020 ).

To date, the literature on the use of AI and BDA for developing supply chain resilience seems to be somewhat fragmented and largely focused on the available computational techniques for supporting different mitigation strategies. However, the rapid evolution of these technologies and the ongoing misinformation-driven disruption of supply chain systems present an opportunity to focus in more detail on what are the exact benefits that these technologies have to offer and what role they can play at the different phases of supply chain resilience efforts, while considering the observed and anticipated challenges regarding their implementation and use. Motivated by the above, the present study adopts a systematic literature review to synthesise prior research on AI, BDA, and supply chain resilience to consolidate existing findings, to inform scholars and practitioners with regards the benefits and challenges of these technologies along each distinct phase of establishing supply chain resilience, and to propose a future research agenda that will shape future work in this field.

The section outlines the systematic literature review (SLR)process adopted in this study. We follow the established guidelines proposed by Tranfield et al., ( 2003 ) which have been used in other SLR studies in varying contexts (e.g., Ahmad et al., 2018 ; Patyal et al., 2021 ; Spanaki et al., 2021 ; Tandon et al., 2020 ). By conducting an evidence-based review, an SLR “identifies key scientific contributions to a field or question, meta-analysis offers a statistical procedure for synthesizing findings in order to obtain overall reliability unavailable from any single study alone” (Tranfield et al., 2003 , p. 209). It is widely accepted that conducting a SLR is a “fundamental scientific activity” (Mulrow, 1994 , p. 597).

The SLR process is illustrated in Fig. ​ Fig.1 1 and consists of 9 steps across three phases, namely, planning (3 steps), conducting (3 steps), and documenting (3 steps). Each of these three phases and 9 steps are discussed in detail in the remainder of this section.

Protocol for systematic literature review

Planning the SLR study

This section presents steps 1, 2 and 3 that are related to the planning of this SLR study. The motivation of this study is to classify and synthesise extant literature on supply chain resilience, through thematic analysis of the primary studies. The main objectives of this study (Step 1), as previously stated, are to (i) establish the body of knowledge of supply chain resilience by identifying and categorising extant research on the topic, (ii) identify the most relevant supply chain resilience articles, (iii) synthesise the reported benefits and challenges of AI and BDA in the context of supply chain resilience and (v) identify the opportunities for future research. To achieve these objectives, the research questions (Step 2) listed in Table ​ Table1 1 will be answered.

Research Questions

As RQ1 is a broad research question, three sub questions (RQ1.1 - RQ1.4) have been identified to answer this question, while RQ2 and RQ3 will provide a synthesis of the reported challenges and benefits of AI and BDA in the context of supply chain resilience and disruptions.

We focus specifically on AI and BDA because these two technologies leverage and create opportunities for exploiting the numerous data streams that typically flow through and within a supply chain system. Namely, they can take advantage of the data that currently exist and flow through information systems such as Enterprise Resource Planning (ERP) ones, and which presently “track more data than we can digest”. ERPs can monitor and alert supply chain and operations management for shipping updates, stock levels, demand and supply (Pettit et al., 2019 ). While currently these systems can inform managers about the past and the present, if enhanced with BDA and AI capabilities, they can also inform them about potential future states and thus incorporate resilience-oriented concepts. At this point, it is also important to define how we interpret AI and BDA, as currently there are numerous definitions in the literature (Collins et al., 2021 ). We understand AI as “the ability of a system to identify, interpret, make inferences, and learn from data to achieve predetermined organizational and societal goals” (Mikalef & Gupta, 2021 ). Similarly, we approach BDA as the portfolio of technologies, techniques, and organisational resources that allow a company to analyse large-scale and complex datasets, so as to improve their performance and develop actionable insights from big data (Mikalef et al., 2018 ).

Conducting the SLR study

This section presents steps 4, 5 and 6 that are related to conducting this SLR study. The search string (Step 4) was developed based on the scope of this study. Keyword combinations (e.g., Artificial intelligence, big data analytics, supply chain resilience) that were used in previous SLR studies (i.e., Baryannis et al., 2019 ; Hosseini & Ivanov, 2019 ; Ngai et al., 2014 ; Sharma et al., 2020 ) in this field were used for searching the databases. Forward and backward citation review was also conducted to ensure we accumulated a relatively complete census of relevant literature (Webster & Watson, 2002 ). A search of the Scopus database retrieved 522 articles. We choose the Scopus database as it is the most extensive database for engineering and management focused academic journal articles. Further, it provides various fields on which the user can search for research papers (Grover & Kar, 2017 ).

Screening of the retrieved publications (Step 5) was achieved by following the best practices proposed by Tranfield et al., ( 2003 ) and Watson and Webster ( 2002 ). We focused exclusively on studies published in CABS ranked journals as it a common practice within the broader field of Business and Management (Hiebl, 2021 ). In addition, most typically, studies published in CABS ranked journals undergo a more rigorous review process. One author screened the articles to identify non-CABS ranked journals. The process resulted in 202 articles being excluded. Two authors then independently screened the articles to remove (i) duplicates (ii) non-English articles, (iii) non-peer reviewed scientific papers, and (iv) full articles not available. The process resulted in 241 articles being excluded. Next, two authors independently reviewed articles and excluded articles not explicitly focused on AI or BDA in the context of supply chain resilience. This process resulted in 56 articles being excluded as they refer to AI and BDA in the article but do not explicitly study these technologies. At the end of the process, 23 primary studies were identified (Step 6). The primary studies are listed in Appendix A.

Documenting the SLR study

This section presents steps 7, 8 and 9 that are related to the planning of this SLR study. Once the primary studies were identified, they were subject to in-depth analysis (Step 7) by three authors to mitigate validity threats due to researcher bias. Further, to mitigate this threat, data and researcher triangulation was established. The primary studies were analysed based on the research questions of this SLR study. The findings were then synthesised (Step 8) and written up as per the aims of this SLR study. Finally,the first author reviewed each activity (Step 7 and 8) to ensure consistency in the analysis of data, consolidation of the findings, and an evidence-based review SLR was written up.

Synthesis of results

This section presents the results from the analysis of the 23 primary studies, which is based on the research questions previously mentioned. The results represent the current state of AI and BDA research in the context of supply chain resilience between 2011 and 2021. We address our first research question: ‘What is the current state of AI and BDA in SC literature between 2011–2021?, by examining the following: (i) publication by year, (ii) publication outlets, (iii) research methodology adopted, (v) data collection techniques, and reported benefits, and (ix) reported challenges.

RQ 1.1 What number of academic studies on AI and BDA for SCR have been published between 2011 and 2021?

The aim of this research question is to establish the annual number of academic studies on AI and BDA within the context of supply chain resilience between 2011 and 2021. Figure ​ Figure2 2 shows the number of publications by year of the primary studies over the 10-year period. This timeline is valuable as it indicates that academic studies on AI and BDA in the context of supply chain resilience were not published in CABS ranked journals between 2011 and 2015. Since 2016 there is an accumulative increase (see amber scale) in publications. In this context there is a significant number of publications (13) in 2021. The 23 primary studies were published between 2016 and 2021 which are based on the inclusion and exclusion criteria used in this study.

Publication period

RQ 1.2 What SC industries has AI and BDA research been applied to?

The aim of this research question is to identify the supply chain industries in which AI and BDA research have been applied to. Figure ​ Figure3 3 shows that most primary studies (13) applied AI and BDA to a mix of supply chain industries, followed by 8 primary studies that applied these technologies to manufacturing. By mix, we refer to supply chain industries whereby studies did not focus on a particular industry (e.g., automotive sector, following a case study approach or purposeful sampling of companies from the sector) but rather investigated these technologies across industries (e.g., automotive, manufacturing, agricultural and others). One study applied these technologies to humanitarian aid supply chains, and one to agricultural supply chains.

Supply Chain industries

RQ 1.3 What journals are publishing AI and BDA related research in the context of the SC resilience & interruptions?

The aim of this research question is to identify what CABS ranked journals are publishing AI and BDA studies in the context of supply chain resilience and interruptions. Figure ​ Figure4 4 shows that The International Journal of Logistics Management published 4 such primary studies, followed by four journals that each published 2 primary studies, and the eleven other journals each published one primary study.

Publication Outlets

RQ 1.4 What research methods and data collection techniques have been used in AI and BDA studies that focus on SCR?

The aim of this research question is to categorize the research method and data collection techniques that have been used to study AI and BDA in the context of supply chain resilience. Figure ​ Figure5 5 shows that quantitative methods (19) are the most popular, followed by qualitative (3) and mixed methods (1).

Methodological Approaches

A deeper analysis of the methods used in the 23 primary studies was conducted to establish the data collection techniques used in the respective studies. Figure ​ Figure6 6 shows that surveys (13) are the most popular techniques, where the data collection instrument is typically a questionnaire. Other data research designs have been used, but to a much lesser degree, namely semi structured interviews with experts (3), case studies (1), experiments (3), and modelling (3). We note that modelling and experiments often refer to simulation-based studies.

Research Methods

RQ 2 What phases of SCR (readiness, response, recovery, adaptability) has AI and BDA been shown to improve?

Our systematic review revealed several benefits of BDA and AI towards supply chain resilience, which, in this section, we present them by organising them across the phases of supply chain resilience. Existing studies indicate that supply chain resilience draws from four separate phases: readiness, response, recovery and adaptability strategies (Chowdhury & Quaddus, 2016 ; Ponomarov & Holcomb, 2009 ). The readiness phase reflects a business’ anticipation of and preparedness for disruptive events (Fahimnia & Jabbarzadeh, 2016 ). Readiness involves identifying and observing any changes happening within the micro- and macro-environment of the business (Maitlis & Sonenshein, 2010 ). Responsiveness indicates how a business enacts its preconceived mitigation strategies when experiencing disruptions (Stone & Rahimifard, 2018 ). The recovery phase entails repairing losses and returning to business-as-usual (Brandon-Jones et al., 2014 ) or moving to a future desired state as soon as possible (Fahimnia & Jabbarzadeh, 2016 ). Further extending the scope of supply chain resilience, Hohenstein et al., ( 2015 ) propose that there is an additional phase, that of growth, whereby businesses proceed with adapting and adjusting their operations and strategies on the basis of their experience during disruptions in order to prepare for future potential disruptions. We refer to this as the adaptation phase, following the approach espoused by Dennehy et al., ( 2021 ).

BDA and AI have proven to support and shape all supply chain resilience phases. Table ​ Table2 2 provides a classification of existing studies across the four resilience phases. As shown, BDA and AI are emerging technologies that contribute towards all the phases of supply chain resilience, including the adaptation phase. Equally, it is shown that BDA is far more explored for its ability to support supply chain resilience comparatively to AI, and that AI for shaping adaptive strategies is somewhat underexplored.

Supply chain resilience phases benefitted most by AI and BDA

With regards to the readiness phase , Dubey et al., ( 2021a , b ) consider BDA as part of the dynamic capabilities of a business, which can minimise disruptions and particularly under volatile conditions, such as the Covid-19 pandemic. Extending these findings, Dennehy et al., ( 2021 ) highlight that the use of BDA as part of supply chain resilience supports the business to anticipate disruptions, specifically because this technology allows decision makers to sense and forecast such events, as well with tracking and monitoring activities within the context of their operations. Such findings directly explain why BDA and AI can help businesses mitigate misinformation along the supply chain within the context of the readiness phase. These technologies are in position to support businesses in anticipating disruptions, rather than remaining idle until these happen, by directly enabling the monitoring of any deviations from the business-as-usual state in the environment, and the recognition of early warning signals (Zouari et al., 2020 ) by leveraging accurate and real-time big data (Belhadi, Kamble, Jabbour, et al., 2021 ). With regards to this and for AI in particular, supervised machine learning techniques allow the removal of generalisations and noise and the analysis of historical data so as to arrive to better outcomes, grounded on the data (Cavalcante et al., 2019 ). In addition, as shown by Bag et al., ( 2021 ), descriptive, predictive and prescriptive analytics can support tracing suppliers’ performance in real time, which effectively contributes towards supply chain resilience as managers can sense disruptive events earlier on. The existing literature indicates similar benefits using AI with regards to readiness. Namely, Modgil et al., ( 2021a , b ) have found that the use of AI supports sensing multidimensional and multi-layered risks in the environment, and that, especially during Covid-19, AI is being used as part of the daily routine to analyse and sense risks.

A critical phase for supply chain resilience is that of the response phase . As Modgil et al., ( 2021a , b ) find, for a supply chain to be capable to respond in light of disruptive events, the business needs to exhibit the appropriate information processing capabilities, that will help align suppliers, retailers and distributors. The authors also found that, despite the misinformation and fake news that resulted in great fluctuations in demand during Covid-19, AI can be employed to cluster consumer behaviour and therefore address such demands more effectively. Despite delays and the shortages in materials, AI has been able to align stakeholders by enriching their information processing capabilities and supporting the rearrangement of distribution channels in a smart and effective way (Modgil, Gupta, Modgil et al., 2021a , b ). Similar findings have been reported by Dennehy et al., ( 2021 ), with BDA facilitating resource reconfiguration in light of environmental changes and, most importantly, doing so in a timely fashion. In other words, emerging technologies such as AI and BDA can support the development of response mechanisms that can mitigate disruptions. This is because these technologies directly contribute towards developing dynamic capabilities that progressively become institutionalised, and then shift into risk resilience capabilities, that enable firms to restructure and reconfigure their resources, if and when needed (Singh & Singh, 2019 ).

As far as the recovery phase is concerned, several studies indicate the value of AI (e.g., Belhadi, Mani, Kamble, et al., 2021 ; Modgil, Singh, et al., 2021, 2021) and BDA (e.g., Dennehy et al., 2021 ; Ivanov, 2017 ; Khan et al., 2021 ; Sheng & Saide, 2021 ). Work in this area illustrates how these technologies facilitate recovery during and after disruptions by enabling firms to rebuild their supply chain operations, reconnect potentially fragmented supply chain components and coordinate recovery plans (Ivanov et al., 2019 ). For example, BDA and AI, coupled with other digital technologies, can speed up the execution of recovery plans to allow the timely mitigation of misinformation and disruption impacts, thus halting further propagation of the negative effects (Ivanov et al., 2019 ). In more detail, some of the benefits of AI and BDA for the recovery phase relate to the last mile delivery, whereby predictive analytics can help with managing disruptions in the workforce, and adopting paperless working patterns to create efficiencies across the supply chain (Modgil, Singh, et al., 2021). However, Rajesh ( 2016 ) has further found that it is not sufficient to use big data or more data for decision making during the recovery phase, but that instead what is needed is the use of the right data indicators, which will ensure accuracy and exactness in executing the recovery. Similarly, Cavalcante et al., ( 2019 ) have further emphasised the need for accurate data as well as the use of AI techniques that reduce potential abstractions from the datasets, and evaluated this approach through simulation and machine learning models, whereby their results indicate that decision making that is based on these approaches can enhance supplier selection when attempting to restore operations to the business-as-usual state, because managers can predict supplier performance following the disruption (Cavalcante et al., 2019 ) and achieve stabilisation and supply continuity (Dolgui et al., 2018 ).

The adaptive phase represents the firm’s efforts towards developing capabilities for dealing with current but also future disruptions: knowledge and experience are extracted from the firm’s current response and then become institutionalised within the business, thus informing future responses (Singh & Singh, 2019 ). Along these lines, using AI techniques has a significant positive impact on the firm’s adaptive capabilities because these techniques allow the firm to learn from the external environment and to reduce the complexity of highly complex systems, and thus facilitate resilience (Belhadi, Mani, Kamble, et al., 2021 ). In addition, AI can contribute and inform the firm’s restorative capacity, directly influencing the supply chain’s recovery (Cavalcante et al., 2019 ; Modgil, Singh, et al., 2021). Specifically, like in earlier phases, BDA and AI indicate ways for optimised resource allocation, especially when resources are scant (Ivanov et al., 2019 ), which is essential and part of the business’ core capabilities for resilient supply chains (Dennehy et al., 2021 ).Dennehy et al. ( 2021 ) in particular have found that BDA supports supply chains, not only to become flexible and adaptable following the recovery phase, but also that this technology can support moving even to a more desirable stage after the disruption. Modgil et al., ( 2021a , b ) argue that, AI can equally support the adaptation of the supply chain following disruption, because it facilitates design thinking when human and non-human entities are involved in highly complex systems, and supporting managers and decision makers to learn directly from AI insights. Such insights may refer to identifying whether there is a gap between the existing information processing capabilities and the information that needs to be processed in the future to respond to disruptions, and how such a gap can be bridged. Further, BDA and AI, can support the adaptive phase by identifying the more vulnerable parts of the supply chain, recognising the required stock levels of (strategic) resources, and developing pathways for responding to future events, building on advance techniques such as simulations and gaming (Zouari et al., 2020 ).

As shown in Table ​ Table2, 2 , several of the analysed studies exhibit an overlap with regards to the benefits stemming from AI and BDA. Specifically, in their majority, studies identify and discuss the benefits of AI and BDA for most if not all of the SCR phases. For example, Dennehy et al., ( 2021 ) indicate that BDA can support SCR with regards to all four phases (readiness, response, recovery and adaptation). With regards to AI, the examined studies show a similar pattern; yet, in most cases scholars identify benefits for three out of the four phases. For instance, Dubey et al., ( 2021a , b ) indicate that AI may contribute towards readiness, response and adaptation, but do not discuss the recovery phase, whereas Cavalcante et al., ( 2019 ) explore benefits during the phases of response, recovery and adaptation but not readiness. The chart in Fig. ​ Fig.7 7 visualises the aforementioned overlap of benefits across SCR phases for the two technologies. It also clearly illustrates that to date, far more studies focus on BDA, and that AI focused studies in their majority explore and consider benefits for recovery.

Spider chart comparing the number of studies across the four phases of SCR for AI and BDA

RQ 3 What are the claimed benefits of AI & BDA in SCR literature?

Across the collected studies, the main benefits identified with regards to supply chain resilience are shown in Fig. ​ Fig.8. 8 . In Table ​ Table3, 3 , we summarise the identified benefits of AI and BDA for SCR. Our analysis revealed that most of the studies identify BDA and AI as supportive of improving visibility and transparency across the supply chain and enabling effective decision-making.

Frequency of reported benefits across studies

Reported benefits based on context and technology

With regards to improving visibility and transparency , Zouari et al., ( 2020 ) have found that the digitalisation of the supply chain, as facilitated by the use of BDA and AI, supports visibility along the supply chain because these technologies contribute towards gathering critical knowledge with respect to environment and the status of operating assets. Similarly, Belhadi et al., ( 2021a , b , c ) have shown that BDA in particular, enhance transparency, which in turn supports supply chain resilience, because visibility combined with transparency enable quick buy-in and commitment of those involved in tackling disruptions, and in turn make recovery speedier and better. Such commitment and buy-in essentially relates to effective decision-making , i.e., the second most frequently cited benefit of BDA and AI. Indeed, the study by Dennehy et al., ( 2021 ) revealed that during crises, BDA, as a technological capability, supports supply chain professionals generate insights and intelligence, which can then empower top management to make decisions informed by data, rather than solely on the basis of experience and/or intuition. Examples of such decision-making include choices regarding the ideal location of facilities and specifically for times of crisis, regarding optimal prediction, distribution and inventory levels, when such decisions are modelled on the basis of big data (Mishra & Singh, 2020 ).

Another major benefit of BDA and AI lies with improving supply chain responsiveness . Responsiveness denotes how the supply chain responds to the demand, for example by adjusting production, modifying operations for both exploiting opportunities and addressing challenges, as well as at the human resource level, by organising and coordinating key personnel to do so (Zouari et al., 2020 ). Focusing on BDA specifically, Rajesh ( 2016 ) approach such responsiveness as corresponding to the speed with which a business addresses customer needs during disruptions and which can be measured as a function of on-time delivery ratio, the contract issue time, the contract approval time and the put-away ratio. The role of BDA in this respect relates to facilitating information sharing among supply chain partners and which results in managing and reducing risks. Considering BDA in combination to AI, studies have shown that responsiveness during crises can only be supported if these technologies are well integrated and interoperable (Frederico et al., 2021 ; Nayal et al., 2021 ). This highlights the importance of the quality, as well as the nature and form of the data used, that will enable such interoperability, and therefore real time information sharing, and thus extracting and responding to generated insights within highly complex environments (Nayal et al., 2021 ).

We found fewer studies investigating the use of BDA and AI for identifying the possible sources of disruption which are however quite illustrative regarding the role of these technologies for identifying risks ahead of and during disruptive events. Dubey et al., ( 2021a , b ), for example, showcase the role of BDA for supporting managers to identify the possible threats, by making visible the vulnerabilities in the supply chain, thereby developing more accurate and relevant business continuity plans. Namely, BDA can be used for identifying risks stemming from the suppliers, but also as a medium for assessing their probability and their impact for operations. In addition, they can be used for identifying bottlenecks and insights for rescheduling tasks and events if and when needed (Cavalcante et al., 2019 ). Others have indicated ways for leveraging information stemming from social media in order to identify emerging risks. For example, Janjua et al., ( 2021 ) have developed a framework that draws information from social media (pertaining to e.g., natural disasters and labour disputes), which they then analyse using AI techniques to analyse threats, capture approximate location of said threats and their timing.

As a result, leveraging AI and BDA for identifying the source of disruption can also speed up the recovery of operations (Singh, 2020 ). Analytics and big data generally facilitate shorter order-to-delivery cycle times and the crafting of demand-driven operations. (Rajesh, 2016 ). In more detail, it is argued that BDA informs the planning, coordination, and control activities of firms during disruptive events, as part of their preparedness, alertness and agility. On the basis of these, firms can develop IT infrastructure capabilities that enable them to support quicker reactions during disruptions (Singh, 2020 ) because this technology, applied in the aforementioned ways, facilitates reduced lead times and the acquiring and processing of reliable information (Dubey, Gunasekaran, et al., 2021 ). In addition, the above approach is applicable even when extending the above methods to incorporate the suppliers’ side, because firms are then able to identify disruptive events impacting their suppliers, and quickly adapt to unpredictable changes within the greater supply chain environment (Sheng & Saide, 2021 ).

How and why BDA and AI help identify the source of disruptions and speed up recovery is addressed by few studies, whereby it is suggested that they provide superior insights regarding threats and improve information processing and quality . For example, it has been found that insights stemming from the processing of large datasets using BDA techniques can improve operation performance especially when such data come from multiple sources (Dubey, Gunasekaran, et al., 2021 ). For instance, BDA insights can inform resource orchestration and allocation, and thus facilitate grounded-on-the-data decision making (Dennehy et al., 2021 ). It is also argued that research and practice need to go beyond than simply leveraging BDA to develop ‘hard to imitate’ capabilities, but instead that other resources are also required, whereby firms exploit BDA in order to improve the quality of information flowing through their supply chain and operations (Singh & Singh, 2019 ). Indeed, BDA may influence supply chain resilience because it improves the quality of information (Bahrami & Shokouhyar, 2021 ) and empowers a strong IT infrastructure (e.g., cyberphysical systems, RFID technology, Industry 4.0 sensors), which in turn enables the collection of precise information with regards to operations and processes, which is more accurate and visually more legible, as data is captured automatically at the source and communicated in real time via graphs and charts (Ivanov et al., 2019 ). These then directly support quicker decision-making because they positively impact the organisation’s information capabilities and, by extension, supply chain resilience, because information planning, coordination and control are its core enablers (Belhadi, Mani, Kamble, et al., 2021 ).

Among the least explored benefits are those of the enhancement of innovative capabilities. The way that BDA and AI influence innovative capabilities is particularly important and interesting because such capabilities relate to improved information processing and quality (Bahrami & Shokouhyar, 2021 ; Belhadi, Mani, Kamble, Belhadi et al., 2021a , b , c ), supply chain visibility and transparency (Belhadi, Mani, Kamble, Belhadi et al., 2021a , b , c ), thereby enabling the development and the actioning of recovery strategies. Under the umbrella of innovative capabilities, scholars identify the firm’s abilities to generate and implement new ideas and insights, processes and products, whereby analytical capabilities are part of these, too (Bahrami & Shokouhyar, 2021 ). Lastly, we identified two studies whereby scholars link BDA and AI to superior and resilient supplier selection. These technologies enable firms to identify and manage suppliers on the basis of insights deriving from spending patterns, service level and penalty data, thereby developing a supply chain ecosystem that can respond to disruptions (Mandal, 2018 ). In addition, machine learning approaches can be particularly useful for supplier selection when combined with simulation techniques to practically examine how decisions regarding suppliers may influence the reliability of the supply chain, thus influencing its performance and overall resilience (Cavalcante et al., 2019 ).

Current challenges

Big data analytics and AI can be seen as emerging technologies with the potential to ‘equalise’ the impacts of uncertainty and enable organisations to predict supply and demand despite misinformation (Belhadi, Mani, Kamble, et al., 2021 ; Verma & Gustafsson, 2020 ). For example, Big Data Analytics can be applied for internal and external process sensing (e.g., inefficiencies), for forecasting, scheduling, real-time resource allocation, and for transforming operational inefficiencies, including real-time process reconfiguration (e.g., automated alerts when there is a high risk of failure) (Conboy et al., 2020 ). Other application areas are the inventory, capacity and labour scheduling, and sourcing. Sourcing, in particular, is potentially one of the areas of most concern during disruptions, and Big Data Analytics can support decision makers to measure risks and negotiate with suppliers “by providing factual leverage” (Sanders, 2016 , p. 32). In addition, other benefits include coordination and knowledge sharing across the entire supply chain (Chen et al., 2015 ) as shown from several of the identified studies (e.g., Belhadi, Kamble, Jabbour, et al., 2021 ; Mandal, 2018 ; Rajesh, 2016 ; Singh & Singh, 2019 ). Despite the benefits from AI and BDA in SCR, most companies are facing difficulties owing to the large investments and challenges related to their deployment and integration (Cadden et al., 2021 ), privacy and security issues, lack of appropriate business cases (Dennehy, 2020 ), in-house capabilities (Rajesh, 2016 ), and sustainability (Patyal et al., 2021 ).

The challenge of identifying appropriate use cases

While AI and BDA can directly influence and impact positively on a company’s supply chain resilience, it is important for the company to identify relevant application areas in order to benefit from these technologies. This can help the company avoid potential issues arising from bandwagon effects due to the adoption of emerging technologies. Since there is a pressure from other organisations that have already adopted those technologies (Abrahamson & Bartner, 1990 ), organisations need to primarily make an assessment of the usefulness of the technological innovations for building SCR in accordance to their requirements (Abrahamson, 1991 ). We consider that the use of BDA can be useful in this respect by helping identify such use cases. Recent studies have shown that BDA can be employed to: support organizations to prioritise and coordinate activities on specific projects (Dennehy et al., 2021 ; Zamani et al., 2021 ), sense and respond to changes in the business environment (Barlette & Baillette, 2022 ; Zamani et al., 2022 ), and create business value (Oesterreich et al., 2022 ; Papadopoulos et al., 2022 ). We thus posit that BDA can be both the instrument that helps identify a problem and be part of the solution that addresses that problem in the context of supply chain resilience.

The challenge of scarce resources and investments

The funding practices adopted by businesses for the development of BDA contradict the traditional funding model (Dennehy et al., 2021 ). With new ideas and capabilities emerging from the use of BDA and AI, the business models have evolved which in turn demands for new management skills along with the technical competencies (Bahrami & Shokouhyar, 2021 ). The feasibility of adopting BDA by understanding the time to acquire and develop SCR and expected return on investment is a critical component. In fact, there are claims that many organisations have failed in realising the feasibility of BDA to meet supply chain resilience (Mikalef et al., 2020 ; Ross et al., 2013 ). The exploitative capabilities of the organisation (in terms of what, how and when) to harness the potential of BDA is crucial considering the huge investments made to build SCR. Organisations face enormous challenges in extracting and translating the information into decision making in relation to managing supply chain networks and there are many instances where the organisations have failed to yield positive return on investment (Dubey, Bryde, et al., 2021 ).

Further, the use of AI and BDA helps in the integration of key areas like managing supply chain networks and knowledge resources. However, for the effective use of these technologies a robust collaboration with the supply chain networks, knowledge resource capabilities and infrastructure is required (Sheng & Saide, 2021 ). Moreover, if there is a lack of knowledge about the use of real time big data for generating insights and if there are knowledge gaps in the adoption of the AI-based platforms, survivability of SCR may be impacted (Pinto et al., 2019 ). As such, a major challenge is that of the skill sets and expertise required in handling data analytics to address supply chain disruption (Ergun et al., 2009 ).

The challenge of organisational culture and change

There is a need to understand the importance of the organisational cultural change that is critical to harness the value of big data analytics (Vidgen et al., 2017 ). For instance, digital maturity helps in the adoption of digital supply chains and has a stronger impact on SCR. Developing digital maturities implies improving information sharing and data architectures, formalising processes, training and engaging all employees towards a digital mindset (Zouari et al., 2020 ). However, many companies make plans to implement digitalisation without considering the need to develop and enhance their degree of digital maturity. Additionally, a data-driven culture in firms may give rise to makeshift supplier-customer relationships thus affecting the bargaining power of firms represented by supplier selection predictive models based smart contracts (Cavalcante et al., 2019 ).

Challenges in relation to the wider ecosystem

The mere development of AI and BDA capabilities is not sufficient to prevent or solve the undefined impacts of exogenous shocks (Lawson et al., 2019 ; Sohrabi et al., 2020 ). Prior to considering AI and BDA for building up SCR, practitioners need to analyse the level of stress the supply chain can absorb and evaluate the degree of reconfiguration for the supply chains to withstand a disruption as well as their capability of analysing changing dynamics. It is essential for all the supply chain partners of a firm, including its lower tier suppliers, to implement a data-driven supply chain (Khan et al., 2021 ) when the firm develops AI and BDA capabilities for building SCR. The supply chain operations in risk management are also benefitted by supply chain resilience arising due to the collaboration among supply chain partners (Yen & Zeng, 2011 ). However, lower tier suppliers may not posses the required technological sophistication and be exposed to incompatible interface standards, legacy systems etc, nor have access to skilled resources required for developing AI and BDA capabilities. Moreover, poor decision-making and unreliable contingencies may arise due to inaccuracy of information and data shared that feed AI algorithms amidst uncertainty in supply chain.

Conclusions and future research

This study performed a systematic literature review on the contributions of artificial intelligence and big data analytics in supply chain resilience. We note that our study is not the first systematic literature in the domain of supply chain management with the focus on the use of technologies for addressing disruptions and enabling resilience. For example, Katsaliaki et al., ( 2021 ) conducted a similar investigation and identified some of the most popular modelling techniques and IT tools used for enhancing resilience. However, in our study, we focus specifically on AI and BDA. This allows us to provide a more nuanced understanding specifically with regards to these two technologies’ role towards supply chain resilience, and in turn, delineating the challenges for adopting them as part of supply chain management.

When investigating the thematic area of supply chain disruptions and resilience, this study can serve as a normative reference for the operations and supply chain disciplines. To this end, three broad-based research questions were identified. The first research question explored the current state of AI and BDA in the supply chain literature during the last decade. This research question investigated to which supply chain industries has AI and BDA been applied, which journals are publishing research on AI and BDA with a specific focus on SC resilience and disruptions, and what data collection techniques and research methods have been used in these studies. This research question has been addressed by providing a detailed summary in Sect.4.1 to 4.4 using Figs. ​ Figs.2, 2 , ​ ,3, 3 , ​ ,4, 4 , ​ ,5 5 and ​ and6. 6 . The second research question was aimed at understanding the phases of supply chain resilience that AI and BDA have improved. This question was addressed by integrating insights derived from the posited advantages of AI and BDA across the readiness, response, recovery, and adaptation phases of supply chain resilience in Sect.4.5 and summarizing them in Table ​ Table2. 2 . The third research question sought to reveal the benefits of AI and BDA in supply chain resilience. This research question was answered by delineating and summarizing key themes pertaining to benefits of AI and BDA in SCR in Sect.4.6 using Fig. ​ Fig.8; 8 ; Table ​ Table3 3 .

The findings of this systematic literature review should be considered in light of its methodological limitations. First this review focussed only on articles in CABS ranked journals available in Scopus database. Other databases such as ACM and IEEE could have been included for an exhaustive search of papers. Second, the review considered “artificial intelligence”, “big data analytics” and “Supply chain resilience” as the keywords for querying the database and didn’t consider other related and interchangeably used terms such as “machine learning”, “business intelligence” or “natural language processing” as keywords. These limitations may be addressed in future research to mitigate the shortcomings of relying on a single database and a set of few umbrella keywords. Moreover, future research may inform the security and privacy related aspects of AI and BDA adoption in supply chain, thus aiding in authenticated use of supply chain systems and avoid data breaches.

This study is an endeavour to encapsulate the research conducted by leading researchers and published in top publication outlets in the field of business. The study has several implications including a need for broader coverage of data collection and methodological approaches such as case-study approach, simulation, and mixed methods. For instance, research based on case-study and mixed method approaches can supplement the understanding of barriers in adoption of AI and BDA for improving supply chain resilience. Moreover, amidst an era of unprecedented exogenous shocks to businesses, there is a growing interest in the use of AI and BDA for supporting supply chain resilience by speeding up recovery times, supplier selection, improving supply chain visibility, transparency, responsiveness. We believe that the structured insights of this review will aid academics and practitioners in the field of supply chain management to develop AI and BDA based interventions for supply chain resilience.

Concluding, we underline that the majority of the studies investigated relate to the use of BDA and AI to superior decision making (e.g., Bag et al., 2021 ; Nayal et al., 2021 ; Singh, 2020 ). However, insights from these technologies may not be sufficient. Supply chain disruptions are often characterised by an impetus to make accurate but quick decisions, under complex and difficult conditions. While BDA and AI can help clarify uncertainties and reduce risks by filling in informational gaps, whether, when and how an organisation will move from insights to actions rests with its decision makers, who need to make sense of such insights but often feel more comfortable turning to their intuitive judgement and prior experience to decide on next steps (Constantiou et al., 2019 ; Zamani et al., 2021 ). We would thus like to invite future research in this area, that will delve deeper into the behavioural perspective and decision-making to explore supply chain and operations decision makers’ behaviours towards the use of emerging technologies during disruptions.

Appendix A: Studies included in the systematic literature review

• Bag et al., ( 2021 ). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). 10.1108/IJLM-02-2021-0095.
• Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Information Technology & People , ahead-of-print (ahead-of-print). 10.1108/ITP-01-2021-0048.
• Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change , 163 , 120,447. 10.1016/j.techfore.2020.120447.
• Belhadi, A., Mani, V., Kamble, S. S., Khan, S. (A) R., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research . 10.1007/s10479-021-03956-x.
• Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management , 49 , 86–97. 10.1016/j.ijinfomgt.2019.03.004.
• Dennehy et al., ( 2021 ). Supply chain resilience in mindful humanitarian aid organizations: The role of big data analytics. International Journal of Operations & Production Management , 41 (9), 1417–1441. 10.1108/IJOPM-12-2020-0871.
• Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M. (2021). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management , 96 , 135–146. 10.1016/j.indmarman.2021.05.003.
• Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research , 59 (1), 110–128. 10.1080/00207543.2019.1582820.
• Frederico, G. F., Kumar, V., Garza-Reyes, J. A., Kumar, A., & Agrawal, R. (2021). Impact of I4.0 technologies and their interoperability on performance: Future pathways for supply chain resilience post-COVID-19. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). 10.1108/IJLM-03-2021-0181.
• Ivanov ( 2017 ). Simulation-based single vs. Dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management , 11 (1), 24–43. 10.1504/IJISM.2017.083005.
• Ivanov, D., Dolgui, A., & Sokolov, (B) ( 2019 ). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research , 57 (3), 829–846. 10.1080/00207543.2018.1488086.
• Janjua, N. K., Nawaz, F., & Prior, D. D. (2021). A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterprise Information Systems , 0 (0), 1–22. 10.1080/17517575.2021.1959652.
• Khan, S. A. R., Yu, Z., Umar, M., Lopes de Sousa Jabbour, (A) B., & Mor, R. S. (2021). Tackling post-pandemic challenges with digital technologies: An empirical study. Journal of Enterprise Information Management , ahead-of-print (ahead-of-print). 10.1108/JEIM-01-2021-0040.
• Mandal ( 2018 ). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation. Information Technology & People , 32 (2), 297–318. 10.1108/ITP-11-2017-0386.
• Mishra, S., & Singh, S. P. (2020). A stochastic disaster-resilient and sustainable reverse logistics model in big data environment. Annals of Operations Research . 10.1007/s10479-020-03573-0.
• Modgil et al., ( 2021 ). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management , ahead-of-print (ahead-of-print). 10.1108/IJPDLM-12-2020-0434.
• Modgil, S., Singh, R. K., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). 10.1108/IJLM-02-2021-0094.
• Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, (B) E., Kazancoglu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). 10.1108/IJLM-12-2020-0493.
• Sheng, M. L., & Saide, S. (2021). Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise. Journal of Business Research , 137 , 567–578. 10.1016/j.jbusres.2021.08.041.
• Singh ( 2020 ). Developing Business Risk Resilience through Risk Management Infrastructure: The Moderating Role of Big Data Analytics. Information Systems Management , 0 (0), 1–19. 10.1080/10580530.2020.1833386.
• Singh & Singh ( 2019 ). Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation. Benchmarking: An International Journal , 26 (7), 2318–2342. 10.1108/BIJ-10-2018-0346.
• Rajesh ( 2016 ). Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications , 20 , 42–58. 10.1016/j.elerap.2016.09.006.
• Zouari et al., ( 2020 ). Does digitalising the supply chain contribute to its resilience? International Journal of Physical Distribution & Logistics Management , 51 (2), 149–180. 10.1108/IJPDLM-01-2020-0038.

Authors’ contribution

All authors contributed equally to the study conception and design. Material preparation, data collection and analysis were performed by E.Z., C.S. and D.D. S.G. developed the manuscript’s conclusions. The first draft of the manuscript was written by all authors and all authors commented on previous versions of the manuscript. D.D. critically revised the manuscript. All authors read and approved the final manuscript.

Fundingand/or competing interests

The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.

Data availability

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Efpraxia D. Zamani, Email: [email protected] .

Conn Smyth, Email: [email protected] .

Samrat Gupta, Email: ni.ca.amii@gtarmaS .

Denis Dennehy, Email: [email protected] .

• Abrahamson E. Managerial Fads and Fashions: The Diffusion and Rejection of Innovations. Academy of Management Review. 1991; 16 (3):586–612. doi: 10.5465/amr.1991.4279484. [ CrossRef ] [ Google Scholar ]
• Abrahamson, E., Bartner, L. R., & DO BANDWAGON DIFFUSIONS ROLL? HOW FAR DO THEY GO? AND WHEN DO THEY ROLL BACKWARDS?. (1990). WHEN : A COMPUTER SIMULATION. Academy of Management Proceedings , 1990 (1), 155–159. 10.5465/ambpp.1990.4978478
• Ahmad MO, Dennehy D, Conboy K, Oivo M. Kanban in software engineering: A systematic mapping study. Journal of Systems and Software. 2018; 137 :96–113. doi: 10.1016/j.jss.2017.11.045. [ CrossRef ] [ Google Scholar ]
• Amankwah-Amoah, J., Khan, Z., & Wood, G. (2020). COVID-19 and business failures: The paradoxes of experience, scale, and scope for theory and practice. European Management Journal , Article in Press , 1–6. Scopus. 10.1016/j.emj.2020.09.002
• Azadegan A, Syed TA, Blome C, Tajeddini K. Supply chain involvement in business continuity management: Effects on reputational and operational damage containment from supply chain disruptions. Supply Chain Management: An International Journal. 2020; 25 (6):747–772. doi: 10.1108/SCM-08-2019-0304. [ CrossRef ] [ Google Scholar ]
• Bag, S., Dhamija, P., Luthra, S., & Huisingh, D. (2021). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of i>-print(ahead-of-print ). 10.1108/IJLM-02-2021-0095
• Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Information Technology & People, ahead-of i>-print(ahead-of-print ). 10.1108/ITP-01-2021-0048
• Barlette Y, Baillette P. Big data analytics in turbulent contexts: Towards organizational change for enhanced agility. Production Planning & Control. 2022; 33 (2–3):105–122. doi: 10.1080/09537287.2020.1810755. [ CrossRef ] [ Google Scholar ]
• Baryannis G, Validi S, Dani S, Antoniou G. Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research. 2019; 57 (7):2179–2202. doi: 10.1080/00207543.2018.1530476. [ CrossRef ] [ Google Scholar ]
• Belhadi A, Kamble S, Wamba F, Queiroz MM. Building supply-chain resilience: An artificial intelligence-based technique and decision-making framework. International Journal of Production Research. 2021; 0 (0):1–21. doi: 10.1080/00207543.2021.1950935. [ CrossRef ] [ Google Scholar ]
• Belhadi A, Kamble S, Jabbour CJC, Gunasekaran A, Ndubisi NO, Venkatesh M. Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change. 2021; 163 :120447. doi: 10.1016/j.techfore.2020.120447. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Belhadi A, Mani V, Kamble SS, Khan SAR, Verma S. Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research. 2021 doi: 10.1007/s10479-021-03956-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brandon-Jones E, Squire B, Autry CW, Petersen KJ. A Contingent Resource-Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain Management. 2014; 50 (3):55–73. doi: 10.1111/jscm.12050. [ CrossRef ] [ Google Scholar ]
• Cadden, T., Dennehy, D., Mantymaki, M., & Treacy, R. (2021). Understanding the influential and mediating role of cultural enablers of AI integration to supply chain. International Journal of Production Research , 1–29. 10.1080/00207543.2021.1946614
• Cavalcante IM, Frazzon EM, Forcellini FA, Ivanov D. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management. 2019; 49 :86–97. doi: 10.1016/j.ijinfomgt.2019.03.004. [ CrossRef ] [ Google Scholar ]
• Chen DQ, Preston DS, Swink M. How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems. 2015; 32 (4):4–39. doi: 10.1080/07421222.2015.1138364. [ CrossRef ] [ Google Scholar ]
• Chowdhury MMH, Quaddus M. Supply chain readiness, response and recovery for resilience. Supply Chain Management: An International Journal. 2016; 21 (6):709–731. doi: 10.1108/SCM-12-2015-0463. [ CrossRef ] [ Google Scholar ]
• Collins C, Dennehy D, Conboy K, Mikalef P. Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management. 2021; 60 :102383. doi: 10.1016/j.ijinfomgt.2021.102383. [ CrossRef ] [ Google Scholar ]
• Conboy K, Mikalef P, Dennehy D, Krogstie J. Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research. 2020; 281 (3):656–672. doi: 10.1016/j.ejor.2019.06.051. [ CrossRef ] [ Google Scholar ]
• Constantiou I, Shollo A, Vendelø MT. Mobilizing intuitive judgement during organizational decision making: When business intelligence is not the only thing that matters. Decision Support Systems. 2019; 121 :51–61. doi: 10.1016/j.dss.2019.04.004. [ CrossRef ] [ Google Scholar ]
• Dennehy D. Ireland Post-Pandemic: Utilizing AI to Kick-Start Economic Recovery. Cutter Business Technology Journal. 2020; 33 (11):22–27. [ Google Scholar ]
• Dennehy D, Oredo J, Spanaki K, Despoudi S, Fitzgibbon M. Supply chain resilience in mindful humanitarian aid organizations: The role of big data analytics. International Journal of Operations & Production Management. 2021; 41 (9):1417–1441. doi: 10.1108/IJOPM-12-2020-0871. [ CrossRef ] [ Google Scholar ]
• Dolgui A, Ivanov D, Sokolov B. Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research. 2018; 56 (1–2):414–430. doi: 10.1080/00207543.2017.1387680. [ CrossRef ] [ Google Scholar ]
• Dubey R, Bryde DJ, Blome C, Roubaud D, Giannakis M. Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management. 2021; 96 :135–146. doi: 10.1016/j.indmarman.2021.05.003. [ CrossRef ] [ Google Scholar ]
• Dubey R, Gunasekaran A, Childe SJ, Fosso Wamba S, Roubaud D, Foropon C. Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research. 2021; 59 (1):110–128. doi: 10.1080/00207543.2019.1582820. [ CrossRef ] [ Google Scholar ]
• Elluru S, Gupta H, Kaur H, Singh SP. Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research. 2019; 283 (1):199–224. doi: 10.1007/s10479-017-2681-2. [ CrossRef ] [ Google Scholar ]
• Ergun O, Karakus G, Keskinocak P, Swann J, Villarreal M. Humanitarian Supply Chain Management—An Overview. In: Barnhart C, Clausen U, Lauther U, Möhring RH, editors. Models and Algorithms for Optimization in Logistics . Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik; 2009. [ Google Scholar ]
• Fadaki M, Rahman S, Chan C. Leagile supply chain: Design drivers and business performance implications. International Journal of Production Research. 2020; 58 (18):5601–5623. doi: 10.1080/00207543.2019.1693660. [ CrossRef ] [ Google Scholar ]
• Fahimnia B, Jabbarzadeh A. Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research Part E: Logistics and Transportation Review. 2016; 91 :306–324. doi: 10.1016/j.tre.2016.02.007. [ CrossRef ] [ Google Scholar ]
• Fosso Wamba S, Gunasekaran A, Dubey R, Ngai EWT. Big data analytics in operations and supply chain management. Annals of Operations Research. 2018; 270 (1–2):1–4. doi: 10.1007/s10479-018-3024-7. [ CrossRef ] [ Google Scholar ]
• Frederico, G. F., Kumar, V., Garza-Reyes, J. A., Kumar, A., & Agrawal, R. (2021). Impact of I4.0 technologies and their interoperability on performance: Future pathways for supply chain resilience post-COVID-19. The International Journal of Logistics Management , ahead-of i>-print(ahead-of-print ). 10.1108/IJLM-03-2021-0181
• Grover P, Kar AK. Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature. Global Journal of Flexible Systems Management. 2017; 18 (3):203–229. doi: 10.1007/s40171-017-0159-3. [ CrossRef ] [ Google Scholar ]
• Gunessee S, Subramanian N. Ambiguity and its coping mechanisms in supply chains lessons from the Covid-19 pandemic and natural disasters. International Journal of Operations & Production Management. 2020; 40 (7/8):1201–1223. doi: 10.1108/IJOPM-07-2019-0530. [ CrossRef ] [ Google Scholar ]
• Hamdi F, Ghorbel A, Masmoudi F, Dupont L. Optimization of a supply portfolio in the context of supply chain risk management: Literature review. Journal of Intelligent Manufacturing. 2018; 29 (4):763–788. doi: 10.1007/s10845-015-1128-3. [ CrossRef ] [ Google Scholar ]
• Hazen BT, Skipper JB, Boone CA, Hill RR. Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research. 2018; 270 (1–2):201–211. doi: 10.1007/s10479-016-2226-0. [ CrossRef ] [ Google Scholar ]
• Hiebl, M. R. W. (2021). Sample Selection in Systematic Literature Reviews of Management Research. Organizational Research Methods , 1094428120986851. 10.1177/1094428120986851
• Hohenstein NO, Feisel E, Hartmann E, Giunipero L. Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management. 2015; 45 (1/2):90–117. doi: 10.1108/IJPDLM-05-2013-0128. [ CrossRef ] [ Google Scholar ]
• Hosseini, S., & Ivanov, D. (2019). A New Resilience Measure for Supply Networks With the Ripple Effect Considerations: A Bayesian Network Approach.Annals of Operation Research. https://aquila.usm.edu/fac_pubs/16506
• Ioannidis C, Pym D, Williams J, Gheyas I. Resilience in information stewardship. European Journal of Operational Research. 2019; 274 (2):638–653. doi: 10.1016/j.ejor.2018.10.020. [ CrossRef ] [ Google Scholar ]
• Ivanov D. Simulation-based single vs. Dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management. 2017; 11 (1):24–43. doi: 10.1504/IJISM.2017.083005. [ CrossRef ] [ Google Scholar ]
• Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review. 2020; 136 :101922. doi: 10.1016/j.tre.2020.101922. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ivanov D, Dolgui A, Sokolov B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research. 2019; 57 (3):829–846. doi: 10.1080/00207543.2018.1488086. [ CrossRef ] [ Google Scholar ]
• Janjua NK, Nawaz F, Prior DD. A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterprise Information Systems. 2021; 0 (0):1–22. doi: 10.1080/17517575.2021.1959652. [ CrossRef ] [ Google Scholar ]
• Jüttner U, Peck H, Christopher M. Supply chain risk management: Outlining an agenda for future research. International Journal of Logistics Research and Applications. 2003; 6 (4):197–210. doi: 10.1080/13675560310001627016. [ CrossRef ] [ Google Scholar ]
• Kapoor K, Bigdeli AZ, Dwivedi YK, Raman R. How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions. Annals of Operations Research. 2021 doi: 10.1007/s10479-021-04397-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Katsaliaki K, Galetsi P, Kumar S. Supply chain disruptions and resilience: A major review and future research agenda. Annals of Operations Research. 2021 doi: 10.1007/s10479-020-03912-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Khan, S. A. R., Yu, Z., Umar, M., Lopes de Sousa Jabbour, A. B., & Mor, R. S. (2021). Tackling post-pandemic challenges with digital technologies: An empirical study. Journal of Enterprise Information Management , ahead-of i>-print(ahead-of-print ). 10.1108/JEIM-01-2021-0040
• Kirk CP, Rifkin LS. I’ll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Business Research. 2020; 117 :124–131. doi: 10.1016/j.jbusres.2020.05.028. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lawson B, Potter A, Pil FK, Holweg M. Supply chain disruptions: The influence of industry and geography on firm reaction speed. International Journal of Operations & Production Management. 2019; 39 (9/10):1076–1098. doi: 10.1108/IJOPM-04-2018-0225. [ CrossRef ] [ Google Scholar ]
• Lee, L. (2016). New Kids on the Blockchain: How Bitcoin’s Technology Could Reinvent the Stock Market. Hastings Business Law Journal, University of Utah College of Law , 12 (2). https://ssrn.com/abstract=2656501 or 10.2139/ssrn.2656501
• Li S, Lin B. Accessing information sharing and information quality in supply chain management. Decision Support Systems. 2006; 42 (3):1641–1656. doi: 10.1016/j.dss.2006.02.011. [ CrossRef ] [ Google Scholar ]
• Lohmer J, Bugert N, Lasch R. Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics. 2020; 228 :107882. doi: 10.1016/j.ijpe.2020.107882. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maitlis S, Sonenshein S. Sensemaking in Crisis and Change: Inspiration and Insights From Weick (1988) Journal of Management Studies. 2010; 47 (3):551–580. doi: 10.1111/j.1467-6486.2010.00908.x. [ CrossRef ] [ Google Scholar ]
• Mandal S. The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation. Information Technology & People. 2018; 32 (2):297–318. doi: 10.1108/ITP-11-2017-0386. [ CrossRef ] [ Google Scholar ]
• Mikalef P, Gupta M. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management. 2021; 58 (3):103434. doi: 10.1016/j.im.2021.103434. [ CrossRef ] [ Google Scholar ]
• Mikalef P, Pappas IO, Krogstie J, Giannakos M. Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and E-Business Management. 2018; 16 (3):547–578. doi: 10.1007/s10257-017-0362-y. [ CrossRef ] [ Google Scholar ]
• Mikalef P, Pappas IO, Krogstie J, Pavlou PA. Big data and business analytics: A research agenda for realizing business value. Information & Management. 2020; 57 (1):103237. doi: 10.1016/j.im.2019.103237. [ CrossRef ] [ Google Scholar ]
• Mishra S, Singh SP. A stochastic disaster-resilient and sustainable reverse logistics model in big data environment. Annals of Operations Research. 2020 doi: 10.1007/s10479-020-03573-0. [ CrossRef ] [ Google Scholar ]
• Modgil, S., Gupta, S., Stekelorum, R., & Laguir, I. (2021a). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management, ahead-ofi>-print(ahead-of-print). 10.1108/IJPDLM-12-2020-0434
• Modgil, S., Singh, R. K., & Hannibal, C. (2021b). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management, ahead-ofi>-print(ahead-of-print). 10.1108/IJLM-02-2021b-0094
• Mulrow CD. Systematic Reviews: Rationale for systematic reviews. Bmj. 1994; 309 (6954):597–599. doi: 10.1136/bmj.309.6954.597. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of i>-print(ahead-of-print ). 10.1108/IJLM-12-2020-0493
• Ngai EWT, Peng S, Alexander P, Moon KKL. Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles. Expert Systems with Applications. 2014; 41 (1):81–91. doi: 10.1016/j.eswa.2013.07.013. [ CrossRef ] [ Google Scholar ]
• Oesterreich TD, Anton E, Teuteberg F, Dwivedi YK. The role of the social and technical factors in creating business value from big data analytics: A meta-analysis. Journal of Business Research. 2022; 153 :128–149. doi: 10.1016/j.jbusres.2022.08.028. [ CrossRef ] [ Google Scholar ]
• Papadopoulos T, Baltas KN, Balta ME. The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management. 2020; 55 :102192. doi: 10.1016/j.ijinfomgt.2020.102192. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Papadopoulos T, Singh SP, Spanaki K, Gunasekaran A, Dubey R. Towards the next generation of manufacturing: Implications of big data and digitalization in the context of industry 4.0. Production Planning & Control. 2022; 33 (2–3):101–104. doi: 10.1080/09537287.2020.1810767. [ CrossRef ] [ Google Scholar ]
• Patyal, V., Sarma, P., Modgil, S., Nag, T., & Dennehy, D. (2021). Mapping the links between Industry 4.0, Circular Economy and Sustainability: A Systematic Literature Review.Journal of Enterprise Information Management
• Pettit TJ, Croxton KL, Fiksel J. The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience. Journal of Business Logistics. 2019; 40 (1):56–65. doi: 10.1111/jbl.12202. [ CrossRef ] [ Google Scholar ]
• Pinto MMA, Kovaleski JL, Yoshino RT, Pagani RN. Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method. Sustainability. 2019; 11 (12):3485. doi: 10.3390/su11123485. [ CrossRef ] [ Google Scholar ]
• Ponomarov SY, Holcomb MC. Understanding the concept of supply chain resilience. The International Journal of Logistics Management. 2009; 20 (1):124–143. doi: 10.1108/09574090910954873. [ CrossRef ] [ Google Scholar ]
• Purvis, L., Spall, S., Naim, M., & Spiegler, V. (2016). Developing a resilient supply chain strategy during ‘boom’ and ‘bust.’. Production Planning & Control , 0–0. 10.1080/09537287.2016.1165306
• Rajesh R. Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications. 2016; 20 :42–58. doi: 10.1016/j.elerap.2016.09.006. [ CrossRef ] [ Google Scholar ]
• Ralston P, Blackhurst J. Industry 4.0 and resilience in the supply chain: A driver of capability enhancement or capability loss? International Journal of Production Research. 2020; 58 (16):5006–5019. doi: 10.1080/00207543.2020.1736724. [ CrossRef ] [ Google Scholar ]
• Rezapour S, Farahani RZ, Pourakbar M. Resilient supply chain network design under competition: A case study. European Journal of Operational Research. 2017; 259 (3):1017–1035. doi: 10.1016/j.ejor.2016.11.041. [ CrossRef ] [ Google Scholar ]
• Roden S, Nucciarelli A, Li F, Graham G. Big data and the transformation of operations models: A framework and a new research agenda. Production Planning & Control. 2017; 28 (11–12):929–944. doi: 10.1080/09537287.2017.1336792. [ CrossRef ] [ Google Scholar ]
• Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). December 1). You May Not Need Big Data After All.Harvard Business Review. https://hbr.org/2013/12/you-may-not-need-big-data-after-all
• Sanders NR. How to Use Big Data to Drive Your Supply Chain. California Management Review. 2016; 58 (3):26–48. doi: 10.1525/cmr.2016.58.3.26. [ CrossRef ] [ Google Scholar ]
• Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research. 2020; 119 :104926. doi: 10.1016/j.cor.2020.104926. [ CrossRef ] [ Google Scholar ]
• Sheng ML, Saide S. Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise. Journal of Business Research. 2021; 137 :567–578. doi: 10.1016/j.jbusres.2021.08.041. [ CrossRef ] [ Google Scholar ]
• Singh N. Developing Business Risk Resilience through Risk Management Infrastructure: The Moderating Role of Big Data Analytics. Information Systems Management. 2020; 0 (0):1–19. doi: 10.1080/10580530.2020.1833386. [ CrossRef ] [ Google Scholar ]
• Singh NP, Singh S. Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation. Benchmarking: An International Journal. 2019; 26 (7):2318–2342. doi: 10.1108/BIJ-10-2018-0346. [ CrossRef ] [ Google Scholar ]
• Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19) International Journal of Surgery. 2020; 76 :71–76. doi: 10.1016/j.ijsu.2020.02.034. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spanaki K, Sivarajah U, Fakhimi M, Despoudi S, Irani Z. Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research. 2021 doi: 10.1007/s10479-020-03922-z. [ CrossRef ] [ Google Scholar ]
• Stecke KE, Kumar S. Sources of Supply Chain Disruptions, Factors That Breed Vulnerability, and Mitigating Strategies. Journal of Marketing Channels. 2009; 16 (3):193–226. doi: 10.1080/10466690902932551. [ CrossRef ] [ Google Scholar ]
• Stone J, Rahimifard S. Resilience in agri-food supply chains: A critical analysis of the literature and synthesis of a novel framework. Supply Chain Management: An International Journal. 2018; 23 (3):207–238. doi: 10.1108/SCM-06-2017-0201. [ CrossRef ] [ Google Scholar ]
• Tandon A, Dhir A, Islam AKMN, Mäntymäki M. Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda. Computers in Industry. 2020; 122 :103290. doi: 10.1016/j.compind.2020.103290. [ CrossRef ] [ Google Scholar ]
• Tranfield D, Denyer D, Smart P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management. 2003; 14 (3):207–222. doi: 10.1111/1467-8551.00375. [ CrossRef ] [ Google Scholar ]
• Verma S, Gustafsson A. Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research. 2020; 118 :253–261. doi: 10.1016/j.jbusres.2020.06.057. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vidgen R, Shaw S, Grant DB. Management challenges in creating value from business analytics. European Journal of Operational Research. 2017; 261 (2):626–639. doi: 10.1016/j.ejor.2017.02.023. [ CrossRef ] [ Google Scholar ]
• Webster J, Watson RT. Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly. 2002; 26 (2):xiii–xxiii. [ Google Scholar ]
• Wenzel, M., Stanske, S., & Liberman, M. B. (2021). Strategic responses to crisis. Strategic Management Journal , 42 (2), 10.1002/smj.3161
• Wong WP, Tan KH, Govindan K, Li D, Kumar A. A conceptual framework for information-leakage-resilience. Annals of Operations Research. 2021 doi: 10.1007/s10479-021-04219-5. [ CrossRef ] [ Google Scholar ]
• Yen, B. P. C., & Zeng, B. (2011). Modeling and Analysis of Supply Chain Risk System under the Influence of Partners’ Collaboration. 2011 44th Hawaii International Conference on System Sciences , 1–10. 10.1109/HICSS.2011.311
• Zamani ED, Griva A, Conboy K. Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure. Information Systems Frontiers. 2022 doi: 10.1007/s10796-022-10255-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zamani ED, Griva A, Spanaki K, O’Raghallaigh P, Sammon D. Making sense of business analytics in project selection and prioritisation: Insights from the start-up trenches. Information Technology & People. 2021 doi: 10.1108/ITP-09-2020-0633. [ CrossRef ] [ Google Scholar ]
• Zarocostas J. How to fight an infodemic. The Lancet. 2020; 395 (10225):676. doi: 10.1016/S0140-6736(20)30461-X. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zouari D, Ruel S, Viale L. Does digitalising the supply chain contribute to its resilience? International Journal of Physical Distribution & Logistics Management. 2020; 51 (2):149–180. doi: 10.1108/IJPDLM-01-2020-0038. [ CrossRef ] [ Google Scholar ]
• Zwanka RJ, Buff C. COVID-19 Generation: A Conceptual Framework of the Consumer Behavioral Shifts to Be Caused by the COVID-19 Pandemic. Journal of International Consumer Marketing. 2021; 33 (1):58–67. doi: 10.1080/08961530.2020.1771646. [ CrossRef ] [ Google Scholar ]

• DOI: 10.1016/j.cjar.2024.100372
• Corpus ID: 270495996

Artificial intelligence and corporate risk-taking: Evidence from China

• Hong Chen , Mengyun Zhang , +1 author Wenhua Wang
• Published in China Journal of Accounting… 1 June 2024
• Business, Computer Science

44 References

Shadow banking business and firm risk-taking: evidence from china, environmental performance and corporate risk-taking: evidence from china, digital innovation and the effects of artificial intelligence on firms’ research and development – automation or augmentation, exploration or exploitation, does digital transformation matter for corporate risk-taking, temperature and corporate risk taking in china, clan culture and risk-taking of chinese enterprises, societal trust and corporate risk-taking: international evidence, artificial intelligence, systemic risks, and sustainability, artificial intelligence in supply chain management: a systematic literature review, reshaping the contexts of online customer engagement behavior via artificial intelligence: a conceptual framework, related papers.

Showing 1 through 3 of 0 Related Papers

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Strategies for humanitarian logistics and supply chain in organizational contexts: pre- and post-disaster management perspectives.

1. Introduction

• What is the best policy for managing disasters in the interior body of an organization?
• In which candidate sites, such as regional branches, operations centers/field offices, external stakeholders, major transportation hubs, and operational units, should the permanent relief centers (PRCs) be opened, and how many different RIs should be propositioned in the established PRCs?
• How many staff should be transported from operations centers/field offices and external stakeholders to affected areas for helping rescue activities and the distribution of RIs separately?
• How many ground vehicles, helicopters, AAs, and EMVs should the organization provide to effectively manage a disaster?
• How many untreated, injured, and unsatisfied demands would the organization have?

2. Literature Review

• Regional branches: These represent decentralized units or branches of the organization located in different regions. They can help ensure that the organization has a presence and can effectively serve its stakeholders across various geographic areas.
• Operational units: These are the frontline teams responsible for executing operational tasks within the organization. They encompass different departments or teams involved in core operational activities, such as production, service delivery, logistics, and customer support.
• Major transportation hubs: These are critical facilities or locations where transportation activities are centralized or concentrated. They play a crucial role in facilitating the movement of goods, services, and people within the organization’s network.
• Operations centers/field Offices: These represent centralized or decentralized facilities that serve as command and control centers for monitoring, coordinating, and managing operational activities. They can include operations centers, command centers, control rooms, and field offices, depending on their specific functions and locations.
• External stakeholders: These are the various entities outside the organization that have a stake or interest in its activities, decisions, or outcomes. External stakeholders can include suppliers, customers, partners, regulatory agencies, government bodies, communities, and other organizations or individuals with whom the organization interacts or collaborates.
• Medical centers: These represent facilities or units within the organization dedicated to providing healthcare services. They can include medical centers, clinics, medical centers, and other healthcare facilities that offer medical treatment, diagnostic services, and patient care.
• Providing a pre- and post-disaster management model inside an organization using the system’s internal capabilities and which is independent of other organizations.
• Presenting a mathematical model for pre-and post-disaster planning in an organization considering operations centers/field offices and external stakeholders as the relief parts.
• Taking into account the personnel stationed at operations centers/field offices, and involving external stakeholders to assist in rescue activities and the distributing relief items.
• Taking into account regional branches, major transportation hubs, operational units, operations centers/field offices, external stakeholders, and medical centers as the affected areas.
• Considering the regional branches, operations centers/field offices, external stakeholders, major transportation hubs, and operational units as candidate sites for opening PRCs.
• Considering two types of injuries in the affected areas.
• Taking the total time that injured staffs waited in EMVs or AAs to arrive medical centers into account.
• Considering different performance levels of the rescue groups of various medical centers.

3. Problem Description and Formulation

3.1. assumptions.

• The main internal parts of the organization are considered for pre-and post-disaster planning.
• The operations centers/field offices, regional branches, external stakeholders, medical centers, major transportation hubs, and operational units are considered to be the affected areas.
• Some potential sites, operations centers/field offices, regional branches, external stakeholders, medical centers, major transportation hubs, and operational units are taken into account as the candidate sites for PRCs.
• The capacities for TRCs are the same.
• Different capacities are considered for PRCs.
• Distribution of RIs are conducted using ground vehicles and helicopters.
• The rescue groups are transferred from medical centers to affected areas using ERVs and AAs.
• The injured are transported from affected areas to medical centers using ERVs and AAs.
• The number of staff in the operations centers/field offices and external stakeholders, number of available rescue groups in medical centers, number of available all-ground vehicles, helicopters, ERVs, and AAs are known.
• The number of injuries and demands in the affected areas are known.
• Donated RIs are transported to TRC and are distributed among areas.

3.2. Notations

3.3. Mathematical Model

4. solution approach, grasshopper optimization algorithm, 5. computation study, 5.1. numerical experiments, 5.2. results and model validation, 5.3. goa parameters tuning, 5.4. comparative experiments, 6. sensitivity analysis, 7. discussions and managerial implications.

• Regional Branches: These act as localized hubs that can quickly respond to regional needs. Most organizations, regardless of their industry, have a decentralized structure with regional branches to ensure efficient operations and responsiveness. That is why these centers can be considered to be candidate zones for PRCs.
• Operational Units: These units are the backbone of an organization’s response mechanism, handling everything from logistics to administration. Their presence is universal across organizations to ensure operational continuity and efficiency. That is why these centers can be considered as candidate zones for PRCs.
• Major Transportation Hubs: Effective disaster management and humanitarian logistics depend on the ability to quickly move resources. Major transportation hubs are crucial for facilitating the rapid distribution of supplies and personnel, making them a vital component in any organization’s logistics network.
• Operational Centers/Field Offices: These centers are pivotal for coordinating on-ground activities and managing logistics. Almost all organizations have some form of operational centers or field offices to oversee their day-to-day activities and emergency responses. This clarification confirms why these centers can be considered to be relief centers.
• External Stakeholders: Collaborations with external stakeholders, such as suppliers, local authorities, and NGOs, are essential for extending an organization’s reach and resources during disasters. This interconnectivity is a common feature in organizational logistics, ensuring that no entity operates in isolation. This clarification confirms why these centers can be considered to be relief centers.
• Medical Centers: Health and safety is paramount during disaster management. Incorporating medical centers ensures that immediate medical needs are met, a necessity for all organizations involved in humanitarian efforts.

8. Conclusions and Future Directions

Author contributions, data availability statement, conflicts of interest.

• Wang, J.; Ng, Y.Y.E. Post-earthquake housing recovery with traditional construction: A preliminary review. Prog. Disaster Sci. 2023 , 18 , 100283. [ Google Scholar ] [ CrossRef ]
• Kotani, H.; Okai, H.; Tamura, M. Activities and roles of mosques in Japan after the recent major earthquakes: A comprehensive study. Prog. Disaster Sci. 2023 , 20 , 100297. [ Google Scholar ] [ CrossRef ]
• Kodag, S.; Mani, S.K.; Balamurugan, G.; Bera, S. Earthquake and flood resilience through spatial Planning in the complex urban system. Prog. Disaster Sci. 2022 , 14 , 100219. [ Google Scholar ] [ CrossRef ]
• Mukherjee, M.; Abhinay, K.; Rahman, M.M.; Yangdhen, S.; Sen, S.; Adhikari, B.R.; Nianthi, R.; Sachdev, S.; Shaw, R. Extent and evaluation of critical infrastructure, the status of resilience and its future dimensions in South Asia. Prog. Disaster Sci. 2023 , 17 , 100275. [ Google Scholar ] [ CrossRef ]
• Jihad, A.; Muksin, U.; Syamsidik; Ramli, M.; Banyunegoro, V.H.; Simanjuntak, A.V.H.; Rusdin, A.A. Tsunami evacuation sites in the northern Sumatra (Indonesia) determined based on the updated tsunami numerical simulations. Prog. Disaster Sci. 2023 , 18 , 100286. [ Google Scholar ] [ CrossRef ]
• Marome, W.; Rodkul, P.; Mitra, B.K.; Dasgupta, R.; Kataoka, Y. Towards a more sustainable and resilient future: Applying the Regional Circulating and Ecological Sphere (R-CES) concept to Udon Thani City Region, Thailand. Prog. Disaster Sci. 2022 , 14 , 100225. [ Google Scholar ] [ CrossRef ]
• Gray, J.; Lloyd, S.; Healey, S.; Opdyke, A. Urban and rural patterns of typhoon mortality in the Philippines. Prog. Disaster Sci. 2022 , 14 , 100234. [ Google Scholar ] [ CrossRef ]
• Ginige, K.; Mendis, K.; Thayaparan, M. An assessment of structural measures for risk reduction of hydrometeorological disasters in Sri Lanka. Prog. Disaster Sci. 2022 , 14 , 100232. [ Google Scholar ] [ CrossRef ]
• Hoeppe, P. Trends in weather related disasters–Consequences for insurers and society. Weather Clim. Extrem. 2016 , 11 , 70–79. [ Google Scholar ] [ CrossRef ]
• Chaudhary, M.T.; Piracha, A. Natural disasters—Origins, impacts, management. Encyclopedia 2021 , 1 , 1101–1131. [ Google Scholar ] [ CrossRef ]
• Hwang, K.-E.; Kim, I. Post-COVID-19 modular building review on problem-seeking framework: Function, form, economy, and time. J. Comput. Des. Eng. 2022 , 9 , 1369–1387. [ Google Scholar ] [ CrossRef ]
• Farazmehr, S.; Wu, Y. Locating and deploying essential goods and equipment in disasters using AI-enabled approaches: A systematic literature review. Prog. Disaster Sci. 2023 , 19 , 100292. [ Google Scholar ] [ CrossRef ]
• Bodin, Ö.; Nohrstedt, D.; Orach, K. A diagnostic for evaluating collaborative responses to compound emergencies. Prog. Disaster Sci. 2022 , 16 , 100251. [ Google Scholar ] [ CrossRef ]
• Daneshvar, M.; Jena, S.D.; Rei, W. A two-stage stochastic post-disaster humanitarian supply chain network design problem. Comput. Ind. Eng. 2023 , 183 , 109459. [ Google Scholar ] [ CrossRef ]
• Anjomshoae, A.; Banomyong, R.; Mohammed, F.; Kunz, N. A systematic review of humanitarian supply chains performance measurement literature from 2007 to 2021. Int. J. Disaster Risk Reduct. 2022 , 72 , 102852. [ Google Scholar ] [ CrossRef ]
• McLoughlin, D. A framework for integrated emergency management. Public Adm. Rev. 1985 , 45 , 165–172. [ Google Scholar ] [ CrossRef ]
• Kovács, G.; Spens, K.M. Humanitarian logistics in disaster relief operations. Int. J. Phys. Distrib. Logist. Manag. 2007 , 37 , 99–114. [ Google Scholar ] [ CrossRef ]
• Balcik, B.; Bozkir, C.D.C.; Kundakcioglu, O.E. A literature review on inventory management in humanitarian supply chains. Surv. Oper. Res. Manag. Sci. 2016 , 21 , 101–116. [ Google Scholar ] [ CrossRef ]
• Yi, W.; Özdamar, L. A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur. J. Oper. Res. 2007 , 179 , 1177–1193. [ Google Scholar ] [ CrossRef ]
• Berkoune, D.; Renaud, J.; Rekik, M.; Ruiz, A. Transportation in disaster response operations. Socio-Econ. Plan. Sci. 2012 , 46 , 23–32. [ Google Scholar ] [ CrossRef ]
• Rawls, C.G.; Turnquist, M.A. Pre-positioning of emergency supplies for disaster response. Transp. Res. Part B Methodol. 2010 , 44 , 521–534. [ Google Scholar ] [ CrossRef ]
• Döyen, A.; Aras, N.; Barbarosoğlu, G. A two-echelon stochastic facility location model for humanitarian relief logistics. Optim. Lett. 2012 , 6 , 1123–1145. [ Google Scholar ] [ CrossRef ]
• Najafi, M.; Eshghi, K.; de Leeuw, S. A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. OR Spectr. 2014 , 36 , 323–356. [ Google Scholar ] [ CrossRef ]
• Camacho-Vallejo, J.-F.; González-Rodríguez, E.; Almaguer, F.-J.; González-Ramírez, R.G. A bi-level optimization model for aid distribution after the occurrence of a disaster. J. Clean. Prod. 2015 , 105 , 134–145. [ Google Scholar ] [ CrossRef ]
• Rezaei-Malek, M.; Tavakkoli-Moghaddam, R.; Zahiri, B.; Bozorgi-Amiri, A. An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Comput. Ind. Eng. 2016 , 94 , 201–215. [ Google Scholar ] [ CrossRef ]
• Tofighi, S.; Torabi, S.A.; Mansouri, S.A. Humanitarian logistics network design under mixed uncertainty. Eur. J. Oper. Res. 2016 , 250 , 239–250. [ Google Scholar ] [ CrossRef ]
• Hu, S.-L.; Han, C.-F.; Meng, L.-P. Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief. Comput. Ind. Eng. 2017 , 111 , 39–49. [ Google Scholar ] [ CrossRef ]
• Noham, R.; Tzur, M. Designing humanitarian supply chains by incorporating actual post-disaster decisions. Eur. J. Oper. Res. 2018 , 265 , 1064–1077. [ Google Scholar ] [ CrossRef ]
• Vahdani, B.; Veysmoradi, D.; Noori, F.; Mansour, F. Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. Int. J. Disaster Risk Reduct. 2018 , 27 , 290–306. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Lei, H.; Wu, Z.; Zhang, D. A robust model predictive control approach for post-disaster relief distribution. Comput. Ind. Eng. 2019 , 135 , 1253–1270. [ Google Scholar ] [ CrossRef ]
• Momeni, B.; Aghsami, A.; Rabbani, M. Designing humanitarian relief supply chains by considering the reliability of route, repair groups and monitoring route. Adv. Ind. Eng. 2019 , 53 , 93–126. [ Google Scholar ]
• Sakiani, R.; Seifi, A.; Khorshiddoust, R.R. Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Comput. Ind. Eng. 2020 , 140 , 106219. [ Google Scholar ] [ CrossRef ]
• Aghajani, M.; Torabi, S.A.; Heydari, J. A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains. Socio-Econ. Plan. Sci. 2020 , 71 , 100780. [ Google Scholar ] [ CrossRef ]
• Heydari, H.; Aghsami, A.; Rabani, M. A mathematical model to optimize debris clearance problem in the disaster response Phase: A case study. J. Ind. Syst. Eng. 2021 , 14 , 1–34. [ Google Scholar ]
• Abazari, S.R.; Aghsami, A.; Rabbani, M. Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-Econ. Plan. Sci. 2021 , 74 , 100933. [ Google Scholar ] [ CrossRef ]
• Abazari, S.R.; Jolai, F.; Aghsami, A. Designing a humanitarian relief network considering governmental and non-governmental operations under uncertainty. Int. J. Syst. Assur. Eng. Manag. 2021 , 13 , 1430–1452. [ Google Scholar ] [ CrossRef ]
• Hajipour, V.; Niaki, S.T.A.; Akhgar, M.; Ansari, M. The healthcare supply chain network design with traceability: A novel algorithm. Comput. Ind. Eng. 2021 , 161 , 107661. [ Google Scholar ] [ CrossRef ]
• Masoumi, M.; Aghsami, A.; Alipour-Vaezi, M.; Jolai, F.; Esmailifar, B. An M/M/C/K queueing system in an inventory routing problem considering congestion and response time for post-disaster humanitarian relief: A case study. J. Humanit. Logist. Supply Chain Manag. 2022 , 12 , 182–219. [ Google Scholar ] [ CrossRef ]
• Bakhshi, A.; Aghsami, A.; Rabbani, M. A scenario-based collaborative problem for a relief supply chain during post-disaster under uncertain parameters: A real case study in Dorud. J. Model. Manag. 2022 , 18 , 906–941. [ Google Scholar ] [ CrossRef ]
• Ehsani, B.; Karimi, H.; Bakhshi, A.; Aghsami, A.; Rabbani, M. Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city. Comput. Ind. Eng. 2023 , 175 , 108821. [ Google Scholar ] [ CrossRef ]
• Sheikholeslami, M.; Zarrinpoor, N. Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty. Socio-Econ. Plan. Sci. 2023 , 86 , 101496. [ Google Scholar ] [ CrossRef ]
• Lohrasbpoor, M.; Arshadi Khamseh, A.; Nemati-Lafmejani, R.; Naderi, B. Four Echelons Humanitarian Network Design Considering Capacitated/lateral Transshipment with a Destruction Radius and ABO Compatibility: Tehran Earthquake. Int. J. Supply Oper. Manag. 2023 , 10 , 1–22. [ Google Scholar ]
• Khalili-Fard, A.; Hashemi, M.; Bakhshi, A.; Yazdani, M.; Jolai, F.; Aghsami, A. Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network. Omega 2024 , 127 , 103111. [ Google Scholar ] [ CrossRef ]
• Altay, N.; Heaslip, G.; Kovács, G.; Spens, K.; Tatham, P.; Vaillancourt, A. Innovation in humanitarian logistics and supply chain management: A systematic review. Ann. Oper. Res. 2024 , 335 , 965–987. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tarei, P.K.; Gumte, K.M.; Patnaik, J.; Oktari, R.S. Analysing barriers to humanitarian logistics for distributing relief aid in pre-and post-disaster situations. Int. J. Disaster Risk Reduct. 2024 , 104 , 104388. [ Google Scholar ] [ CrossRef ]
• Beiki Ashkezari, A.; Zokaee, M.; Rabbani, E.; Rabbani, M.; Aghsami, A. A scenario-based game theory integrating with a location-allocation-routing problem in a pre-and post-disaster humanitarian logistics network under uncertainty. J. Model. Manag. 2024 . [ Google Scholar ] [ CrossRef ]
• Mousavi, S.; Sajadi, S.M.; AlemTabriz, A.; Najafi, S.E. Hybrid mathematical and simulation model for designing a hierarchical network of temporary medical centers in a disaster. J. Simul. 2024 , 18 , 119–135. [ Google Scholar ] [ CrossRef ]
• Liu, B.; Zhou, Y.; Luo, Q.; Huang, H. Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems. J. Comput. Des. Eng. 2023 , 10 , 1767–1789. [ Google Scholar ] [ CrossRef ]
• Ma, B.; Hu, Y.; Lu, P.; Liu, Y. Running city game optimizer: A game-based metaheuristic optimization algorithm for global optimization. J. Comput. Des. Eng. 2023 , 10 , 65–107. [ Google Scholar ] [ CrossRef ]
• Chen, S.; Zheng, J. Sand cat arithmetic optimization algorithm for global optimization engineering design problems. J. Comput. Des. Eng. 2023 , 10 , 2122–2146. [ Google Scholar ] [ CrossRef ]
• Goodarzimehr, V.; Talatahari, S.; Shojaee, S.; Hamzehei-Javaran, S.; Sareh, P. Structural design with dynamic constraints using weighted chaos game optimization. J. Comput. Des. Eng. 2022 , 9 , 2271–2296. [ Google Scholar ] [ CrossRef ]
• Xing, J.; Zhao, Q.; Chen, H.; Zhang, Y.; Zhou, F.; Zhao, H. Utilizing bee foraging behavior in mutational salp swarm for feature selection: A study on return-intentions of overseas Chinese after COVID-19. J. Comput. Des. Eng. 2023 , 10 , 2094–2121. [ Google Scholar ] [ CrossRef ]
• Jia, H.; Wen, Q.; Wu, D.; Wang, Z.; Wang, Y.; Wen, C.; Abualigah, L. Modified beluga whale optimization with multi-strategies for solving engineering problems. J. Comput. Des. Eng. 2023 , 10 , 2065–2093. [ Google Scholar ] [ CrossRef ]
• Eslami, N.; Yazdani, S.; Mirzaei, M.; Hadavandi, E. A modified binary version of aphid-ant mutualism for feature selection: A COVID-19 case study. J. Comput. Des. Eng. 2023 , 10 , 549–577. [ Google Scholar ] [ CrossRef ]
• Wang, S.; Hussien, A.G.; Kumar, S.; AlShourbaji, I.; Hashim, F.A. A modified smell agent optimization for global optimization and industrial engineering design problems. J. Comput. Des. Eng. 2023 , 10 , 2147–2176. [ Google Scholar ] [ CrossRef ]
• Premkumar, M.; Jangir, P.; Sowmya, R.; Alhelou, H.H.; Mirjalili, S.; Kumar, B.S. Multi-objective equilibrium optimizer: Framework and development for solving multi-objective optimization problems. J. Comput. Des. Eng. 2022 , 9 , 24–50. [ Google Scholar ] [ CrossRef ]
• Zheng, R.; Hussien, A.G.; Qaddoura, R.; Jia, H.; Abualigah, L.; Wang, S.; Saber, A. A multi-strategy enhanced African vultures optimization algorithm for global optimization problems. J. Comput. Des. Eng. 2023 , 10 , 329–356. [ Google Scholar ] [ CrossRef ]
• Jia, H.; Li, Y.; Wu, D.; Rao, H.; Wen, C.; Abualigah, L. Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems. J. Comput. Des. Eng. 2023 , 10 , 1315–1349. [ Google Scholar ] [ CrossRef ]
• Ylldlz, B.S.; Mehta, P.; Panagant, N.; Mirjalili, S.; Yildiz, A.R. A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems. J. Comput. Des. Eng. 2022 , 9 , 2452–2465. [ Google Scholar ] [ CrossRef ]
• Gorji, S.A. Challenges and opportunities in green hydrogen supply chain through metaheuristic optimization. J. Comput. Des. Eng. 2023 , 10 , 1143–1157. [ Google Scholar ] [ CrossRef ]
• Mousapour Mamoudan, M.; Ostadi, A.; Pourkhodabakhsh, N.; Fathollahi-Fard, A.M.; Soleimani, F. Hybrid neural network-based metaheuristics for prediction of financial markets: A case study on global gold market. J. Comput. Des. Eng. 2023 , 10 , 1110–1125. [ Google Scholar ] [ CrossRef ]
• Jia, H.; You, F.; Wu, D.; Rao, H.; Wu, H.; Abualigah, L. Improved snow ablation optimizer with heat transfer and condensation strategy for global optimization problem. J. Comput. Des. Eng. 2023 , 10 , 2177–2199. [ Google Scholar ] [ CrossRef ]
• Qiao, S.; Yu, H.; Heidari, A.A.; El-Saleh, A.A.; Cai, Z.; Xu, X.; Mafarja, M.; Chen, H. Individual disturbance and neighborhood mutation search enhanced whale optimization: Performance design for engineering problems. J. Comput. Des. Eng. 2022 , 9 , 1817–1851. [ Google Scholar ] [ CrossRef ]
• Chen, Y.; Lu, C.; Yan, J.; Feng, J.; Sareh, P. Intelligent computational design of scalene-faceted flat-foldable tessellations. J. Comput. Des. Eng. 2022 , 9 , 1765–1774. [ Google Scholar ] [ CrossRef ]
• Sogandi, F.; Shiri, M. Metaheuristic algorithms for a sustainable saffron supply chain network considering government policies and product quality under uncertainty. J. Comput. Des. Eng. 2023 , 10 , 1892–1929. [ Google Scholar ] [ CrossRef ]
• Cho, Y.I.; Nam, S.H.; Cho, K.Y.; Yoon, H.C.; Woo, J.H. Minimize makespan of permutation flowshop using pointer network. J. Comput. Des. Eng. 2022 , 9 , 51–67. [ Google Scholar ] [ CrossRef ]
• Saremi, S.; Mirjalili, S.; Lewis, A. Grasshopper optimisation algorithm: Theory and application. Adv. Eng. Softw. 2017 , 105 , 30–47. [ Google Scholar ] [ CrossRef ]
• Gouran-Orimi, S.; Ghasemi-Marzbali, A. Load Frequency Control of multi-area multi-source system with nonlinear structures using modified Grasshopper Optimization Algorithm. Appl. Soft Comput. 2023 , 137 , 110135. [ Google Scholar ] [ CrossRef ]
• Alrashidi, M.; Alrashidi, M.; Rahman, S. Global solar radiation prediction: Application of novel hybrid data-driven model. Appl. Soft Comput. 2021 , 112 , 107768. [ Google Scholar ] [ CrossRef ]
• Ingle, K.K.; Jatoth, R.K. Non-linear Channel Equalization using Modified Grasshopper Optimization Algorithm. Appl. Soft Comput. 2024 , 153 , 110091. [ Google Scholar ] [ CrossRef ]
• Hirbod, F.; Eshghali, M.; Sheikhasadi, M.; Jolai, F.; Aghsami, A. A state-dependent M/M/1 queueing location-allocation model for vaccine distribution using metaheuristic algorithms. J. Comput. Des. Eng. 2023 , 10 , 1507–1530. [ Google Scholar ] [ CrossRef ]
• Abbaspour, S.; Aghsami, A.; Jolai, F.; Yazdani, M. An integrated queueing-inventory-routing problem in a green dual-channel supply chain considering pricing and delivery period: A case study of construction material supplier. J. Comput. Des. Eng. 2022 , 9 , 1917–1951. [ Google Scholar ] [ CrossRef ]
• Luo, J.; Chen, H.; Xu, Y.; Huang, H.; Zhao, X. An improved grasshopper optimization algorithm with application to financial stress prediction. Appl. Math. Model. 2018 , 64 , 654–668. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Aghsami, A.; Sharififar, S.; Markazi Moghaddam, N.; Hazrati, E.; Jolai, F.; Yazdani, R. Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives. Systems 2024 , 12 , 215. https://doi.org/10.3390/systems12060215

Aghsami A, Sharififar S, Markazi Moghaddam N, Hazrati E, Jolai F, Yazdani R. Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives. Systems . 2024; 12(6):215. https://doi.org/10.3390/systems12060215

Aghsami, Amir, Simintaj Sharififar, Nader Markazi Moghaddam, Ebrahim Hazrati, Fariborz Jolai, and Reza Yazdani. 2024. "Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives" Systems 12, no. 6: 215. https://doi.org/10.3390/systems12060215

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 10 June 2024

Leveraging edge artificial intelligence for sustainable agriculture

• Moussa El Jarroudi   ORCID: orcid.org/0000-0002-9169-8824 1 ,
• Louis Kouadio   ORCID: orcid.org/0000-0001-9669-7807 2   nAff11 ,
• Philippe Delfosse   ORCID: orcid.org/0009-0003-9371-209X 3 ,
• Clive H. Bock   ORCID: orcid.org/0000-0003-1163-268X 4 ,
• Anne-Katrin Mahlein 5 ,
• Xavier Fettweis   ORCID: orcid.org/0000-0002-4140-3813 6 ,
• Benoit Mercatoris   ORCID: orcid.org/0000-0002-3188-4772 7 ,
• Frank Adams 8 ,
• Jillian M. Lenné 9 &
• Said Hamdioui   ORCID: orcid.org/0000-0002-8961-0387 10  

Nature Sustainability ( 2024 ) Cite this article

192 Accesses

1 Altmetric

Metrics details

• Agriculture
• Environmental economics

Effectively feeding a burgeoning world population is one of the main goals of sustainable agricultural practices. Digital technology, such as edge artificial intelligence (AI), has the potential to introduce substantial benefits to agriculture by enhancing farming practices that can improve agricultural production efficiency, yield, quality and safety. However, the adoption of edge AI faces several challenges, including the need for innovative and efficient edge AI solutions and greater investment in infrastructure and training, all compounded by various environmental, social and economic constraints. Here we provide a roadmap for leveraging edge AI at the intersection of food production and sustainability.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

Digital Regenerative Agriculture

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108 , 20260–20264 (2011).

Article   CAS   Google Scholar  

The State of Food and Agriculture 2019. Moving Forward on Food Loss and Waste Reduction (FAO, 2019).

Chhogyel, N. & Kumar, L. Climate change and potential impacts on agriculture in Bhutan: a discussion of pertinent issues. Agric. Food Secur. 7 , 79 (2018).

Article   Google Scholar  

Davis, K. F. et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 13 , 482–488 (2020).

Muluneh, M. G. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric. Food Secur. 10 , 36 (2021).

What is agrobiodiversity? FAO https://www.fao.org/fileadmin/templates/soilbiodiversity/Downloadable_files/agrobiodivesity.pdf (2004).

Byerlee, D., Stevenson, J. & Villoria, N. Does intensification slow crop land expansion or encourage deforestation? Glob. Food Secur. 3 , 92–98 (2014).

UN-Water Blueprint for Acceleration: Sustainable Development Goal 6 Synthesis Report on Water and Sanitation 2023 (United Nations Publications, 2023).

Rosa, L. et al. Energy implications of the 21st century agrarian transition. Nat. Commun. 12 , 2319 (2021).

Giller, K. E. et al. The future of farming: who will produce our food? Food Secur. 13 , 1073–1099 (2021).

Raymond, C. et al. Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ. Res. Lett. 17 , 035005 (2022).

White, K., Habib, R. & Hardisty, D. J. How to SHIFT consumer behaviors to be more sustainable: a literature review and guiding framework. J. Mark. 83 , 22–49 (2019).

Hallak, R., Onur, I. & Lee, C. Consumer demand for healthy beverages in the hospitality industry: examining willingness to pay a premium, and barriers to purchase. PLoS ONE 17 , e0267726 (2022).

Hughner, R. S., McDonagh, P., Prothero, A., Shultz, C. J. II & Stanton, J. Who are organic food consumers? A compilation and review of why people purchase organic food. J. Consum. Behav. 6 , 94–110 (2007).

Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46 , 4–17 (2017).

Vial, G. Understanding digital transformation: a review and a research agenda. J. Strat. Inf. Syst. 28 , 118–144 (2019).

Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V. & Kumar, A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119 , 104926 (2020).

Yamakami, T. An experimental implementation of an edge-based AI engine with edge-cloud coordination. In 2018 18th International Symposium on Communications and Information Technologies (ISCIT) 442–446 (IEEE, 2018).

McCarthy, J. Programs with common sense. In Proc. Teddington Conference on the Mechanization of Thought Processes 75–91 (Her Majesty’s Stationary Office, 1959).

Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3 , 210–229 (1959).

Dechter, R. Learning while searching in constraint-satisfaction-problems. In Proc. Fifth AAAI National Conference on Artificial Intelligence 178–183 (AAAI Press, 1986).

Liu, X. et al. RENO: a high-efficient reconfigurable neuromorphic computing accelerator design. In Proc. 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) 1–6 (2015).

Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12 , 106–122 (2018).

Akopyan, F. et al. TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34 , 1537–1557 (2015).

Davies, M. E. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38 , 82–99 (2018).

Frenkel, C., Legat, J. D. & Bol, D. MorphIC: a 65-nm 738k-synapse/mm 2 quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Trans. Biomed. Circuits Syst. 13 , 999–1010 (2019).

Gebregiorgis, A. et al. Tutorial on memristor-based computing for smart edge applications. Memories 4 , 100025 (2023).

Google Scholar  

Brown, T. B. et al. Language models are few-shot learners. In Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).

Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. In Computer Vol. 55, 18–28 (IEEE Computer Society, 2022).

Hamdioui, S. et al. Memristor for computing: myth or reality? In Design, Automation & Test in Europe Conference & Exhibition (DATE) 722–731 (IEEE, 2017).

Singh, A. et al. Low-power memristor-based computing for edge-AI applications. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE 2021).

Singh, A. et al. CIM-based robust logic accelerator using 28 nm STT-MRAM characterization chip tape-out. In Proc. IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS) 1–5 (IEEE, 2022).

Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation https://doi.org/10.1016/j.xinn.2021.100179 (2021).

Leroux, S., Simoens, P., Lootus, M., Thakore, K. & Sharma, A. TinyMLOps: operational challenges for widespread edge AI adoption. In Proc. IEEE International Parallel and Distributed Processing Symposium Workshops ( IPDPSW ) 1003–1010 (IEEE, 2022).

Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H. & Pasupuleti, V. R. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2 , 100033 (2020).

Ninomiya, S. High-throughput field crop phenotyping: current status and challenges. Breed. Sci. 72 , 3–18 (2022).

Cisternas, I., Velásquez, I., Caro, A. & Rodríguez, A. Systematic literature review of implementations of precision agriculture. Comput. Electron. Agric. 176 , 105626 (2020).

Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4 , 432–439 (2018).

Pérez-Ruíz, M., Slaughter, D. C., Fathallah, F. A., Gliever, C. J. & Miller, B. J. Co-robotic intra-row weed control system. Biosyst. Eng. 126 , 45–55 (2014).

Gázquez, J. A., Castellano, N. N. & Manzano-Agugliaro, F. Intelligent low cost telecontrol system for agricultural vehicles in harmful environments. J. Clean. Prod. 113 , 204–215 (2016).

Akhavizadegan, F., Ansarifar, J., Wang, L., Huber, I. & Archontoulis, S. V. A time-dependent parameter estimation framework for crop modeling. Sci. Rep. 11 , 11437 (2021).

van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177 , 105709 (2020).

Bock, C. H., Barbedo, J. G. A., Del Ponte, E. M., Bohnenkamp, D. & Mahlein, A.-K. From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol. Res. 2 , 9 (2020).

Dandrifosse, S., Bouvry, A., Leemans, V., Dumont, B. & Mercatoris, B. Imaging wheat canopy through stereo vision: overcoming the challenges of the laboratory to field transition for morphological features extraction. Front. Plant Sci . https://doi.org/10.3389/fpls.2020.00096 (2020).

Ye, Y., Bruzzone, L., Shan, J., Bovolo, F. & Zhu, Q. Fast and robust matching for multimodal remote sensing image registration. IEEE Trans. Geosci. Remote Sens. 57 , 9059–9070 (2019).

Carlier, A., Dandrifosse, S., Dumont, B. & Mercatoris, B. Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing. Front. Plant Sci . https://doi.org/10.3389/fpls.2023.1204791 (2023).

Raj, M. et al. A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. J. Netw. Comput. Appl. 187 , 103107 (2021).

Tardaguila, J., Stoll, M., Gutiérrez, S., Proffitt, T. & Diago, M. P. Smart applications and digital technologies in viticulture: a review. Smart Agric. Technol. 1 , 100005 (2021).

Khanna, A. & Kaur, S. Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Comput. Electron. Agric. 157 , 218–231 (2019).

Bao, J. & Xie, Q. Artificial intelligence in animal farming: a systematic literature review. J. Clean. Prod. 331 , 129956 (2022).

Yépez-Ponce, D. F., Salcedo, J. V., Rosero-Montalvo, P. D. & Sanchis, J. Mobile robotics in smart farming: current trends and applications. Front. Artif. Intell . https://doi.org/10.3389/frai.2023.1213330 (2023).

Duong, L. N. K. et al. A review of robotics and autonomous systems in the food industry: from the supply chains perspective. Trends Food Sci. Technol. 106 , 355–364 (2020).

Food robotics: global market unit volume 2020–2030. Statista https://www.statista.com/statistics/1290022/food-robotics-global-market-unit-volume/ (2022).

Zhou, Z. et al. Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107 , 1738–1762 (2019).

Lin, Y. H. et al. Performance impacts of inalog ReRAM non-ideality on neuromorphic computing. IEEE Trans. Electron Devices 66 , 1289–1295 (2019).

Mohanan, V., Budiarto, R. & Aldmour, I. Powering the Internet of Things with 5G Networks (IGI Global, 2017).

Tzachor, A., Devare, M., King, B., Avin, S. & Ó hÉigeartaigh, S. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat. Mach. Intell. 4 , 104–109 (2022).

Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3 , 160018 (2016).

Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5 , 415–424 (2022).

Deichmann, U., Goyal, A. & Mishra, D. Will digital technologies transform agriculture in developing countries? Agr. Econ. 47 , 21–33 (2016).

Blount-Dorn, K., Detroit Food Policy Council and Lindsey Scalera, Ecology Center Modeling an Equitable Michigan Food System (Michigan State Univ., 2018); https://www.canr.msu.edu/news/modeling-an-equitable-michigan-food-system

Download references

Acknowledgements

A.-K.M. was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2070–390732324. S.H. acknowledges support from the EU Horizon Europe research and innovation programme (grant agreement no. 101070374). C.H.B was supported by the USDA-ARS National Programs through CRIS project 6042-21220-014-000D.

Author information

Louis Kouadio

Present address: Africa Rice Center (AfricaRice), Bouake, Côte d’Ivoire

Authors and Affiliations

SPHERES Research Unit, Department of Environmental Sciences and Management, University of Liège, Arlon, Belgium

Moussa El Jarroudi

Centre for Applied Climate Sciences, Institute for Life and the Environment, University of Southern Queensland, Toowoomba, Queensland, Australia

Office of the Rector, Maison du Savoir, Université du Luxembourg, Esch-sur-Alzette, Grand Duchy of Luxembourg

Philippe Delfosse

ARS-US Horticultural Research Laboratory, USDA, Ft. Pierce, FL, USA

Clive H. Bock

Institute of Sugar Beet Research, Göttingen, Germany

Anne-Katrin Mahlein

SPHERES Research Unit, Department of Geography, University of Liège, Liège, Belgium

Xavier Fettweis

Gembloux Agro-Bio Tech, Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium

Benoit Mercatoris

Lycée Technique Agricole de Gilsdorf, Gilsdorf, Grand Duchy of Luxembourg

Frank Adams

North Oldmoss Croft, Fyvie, Turriff, UK

Jillian M. Lenné

Department of Quantum and Computer Engineering, Delft University of Technology, Delft, Netherlands

Said Hamdioui

You can also search for this author in PubMed   Google Scholar

Contributions

M.E.J., L.K., P.D. and S.H. contributed to the conceptualization and led the writing and revisions. C.H.B., A.-K.M., X.F., B.M., F.A. and J.M.L. contributed to the writing and revisions. All authors have read and agreed to the published version of the paper.

Corresponding authors

Correspondence to Moussa El Jarroudi , Xavier Fettweis or Said Hamdioui .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Sustainability thanks Xu Chen, Yu Jiang and Shangpeng Sun for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

El Jarroudi, M., Kouadio, L., Delfosse, P. et al. Leveraging edge artificial intelligence for sustainable agriculture. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01352-4

Download citation

Received : 07 July 2023

Accepted : 15 April 2024

Published : 10 June 2024

DOI : https://doi.org/10.1038/s41893-024-01352-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability

• Published: 13 June 2024

Cite this article

• Haoyang Wu 1 ,
• Jing Liu 1 &
• Biming Liang 1  

35 Accesses

Explore all metrics

The emergence of Industry 5.0 has ushered in a new era of industrial transformation characterized by the integration of physical and digital systems. In this dynamic environment, the role of artificial intelligence (AI) in supply chain management has become paramount. This research paper explores the impact of AI-driven imperatives on supply chain resilience and sustainability in Industry 5.0. Using a combination of probability analysis (PA), Bayesian-BWM (B-BWM), and Pareto analysis, we identify key AI-driven strategies for enhancing supply chain operations in the ready-made garment (RMG) and footwear sectors in China. The study highlights the importance of continuous monitoring of supply chain operations via the Internet of Things (IoT), adopting a circular production and packaging (CPP) model, and establishing a digital supply chain replica. These strategies improve resilience and contribute to environmental and societal outcomes. Automation and robust cybersecurity for data handling are crucial in the context of Industry 5.0, as they enhance production adaptability and data security. Also, streamlining inventory via RFID technology and harnessing AI to improve workforce safety and operational flow are essential measures. Comprehensive data analysis and forward-looking predictions through AI-driven big data analytics provide insights into consumer demand and energy consumption, ensuring efficient supply chain management. Lastly, while promising, blockchain integration poses challenges in terms of investment and regulatory compliance. However, it is important to consider the ethical implications and regulatory frameworks associated with AI deployment, as well as the need for education and training to bridge the digital gap. Collaboration between governments, industries, and educational institutions is essential to establish a comprehensive Industry 15.0 that benefits all segments of society. This research sheds light on the transformative potential of AI-driven supply chain management in Industry 5.0 and underscores the importance of addressing challenges to maximize its benefits.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Supply chain disruptions and resilience: a major review and future research agenda

Literature review of Industry 4.0 and related technologies

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Aggarwal, S. (2019). A survey-cum-tutorial on approximations to Gaussian Q function for symbol error probability analysis over Nakagami-m fading channels.  IEEE Communications Surveys & Tutorials, 21 (3), 2195–2223.

Article   Google Scholar  

Agrawal, P., & Narain, R. (2023). Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach. International Journal of Productivity and Performance Management, 72 (2), 410–439.

Agrawal, S., Agrawal, R., Kumar, A., Luthra, S., & Garza-Reyes, J. A. (2023). Can industry 5.0 technologies overcome supply chain disruptions?—A perspective study on pandemics, war, and climate change issues. Operations Management Research , 1–16. Early Access

Ahad, T., Busch, P., Blount, Y., & Picoto, W. (2021). Mobile phone-based information systems for empowerment: Opportunities for ready-made garment industries. Journal of Global Information Technology Management, 24 (1), 57–85.

Ahanger, T. A., Aljumah, A., & Atiquzzaman, M. (2022). State-of-the-art survey of artificial intelligent techniques for IoT security. Computer Networks, 206 , 108771.

Ahmed, T., Karmaker, C. L., Nasir, S. B., Moktadir, M. A., & Paul, S. K. (2023). Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective. Computers & Industrial Engineering, 177 , 109055.

Akhtar, P., Ghouri, A. M., Khan, H. U. R., & Amin ul Haq, M., Awan, U., Zahoor, N.,... & Ashraf, A. (2023). Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of Operations Research, 327 (2), 633–657.

Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research , 308 （1-2), 7-39.

Alam, M. F. B., Hosen, M. I., Mridha, J. H., Chowdhury, S. E., & Rahman, M. A. (2023). Assessing the barriers of integrating technological innovations in textiles sector: Implications towards sustainable production. Green Technologies and Sustainability, 1 (3)

Al-Talib, M., Melhem, W. Y., Anosike, A. I., Garza Reyes, J. A., Nadeem, S. P., & kumar, A. (2020). Achieving resilience in the supply chain by applying IoT technology. Procedia CIRP, 91 , 752–757.

Arora, S., & Majumdar, A. (2022). Machine learning and soft computing applications in textile and clothing supply chain: Bibliometric and network analyses to delineate future research agenda. Expert Systems with Applications, 200 ,

Attaran, M. (2020). Digital technology enablers and their implications for supply chain management. Supply Chain Forum, 21(3), 158–172

Awan, U., Kanwal, N., Alawi, S., Huiskonen, J., & Dahanayake, A. (2021). Artificial intelligence for supply chain success in the era of data analytics. The fourth industrial revolution: Implementation of artificial intelligence for growing business success , Studies in Computational Intelligence, 935, 3–21.

Ayyildiz, E., & Erdogan, M. (2022). Identifying and prioritizing the factors to determine best insulation material using Bayesian best worst method. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering , Early Access

Bag, S., Yadav, G., Wood, L. C., Dhamija, P., & Joshi, S. (2020). Industry 4.0 and the circular economy: Resource melioration in logistics. Resources Policy, 68 , 101776.

Bajaj, S., Garg, R., & Sethi, M. (2018). Total quality management: A critical literature review using Pareto analysis. International Journal of Productivity and Performance Management, 67 (1), 128–154.

Balaji, T. K., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40 ,

Balasubramanian, S., Shukla, V., & Kavanancheeri, L. (2023). Improving supply chain sustainability using artificial intelligence: Evidence from the manufacturing sector. Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains: Volume II-Methods for transition and trends (pp. 43–59). Singapore: Springer Nature Singapore.

Google Scholar  

Bastas, A., & Garza-Reyes, J. A. (2022). Impact of the COVID-19 pandemic on manufacturing operations and supply chain resilience: Effects and response strategies. Journal of Manufacturing Technology Management, 33 (5), 962–985.

Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 40: Challenges and opportunities. Artificial Intelligence Review, 54 (5), 3849–3886.

Behl, A., Sampat, B., Pereira, V., & Chiappetta Jabbour, C. J. (2023). The role played by responsible artificial intelligence (RAI) in improving supply chain performance in the MSME sector: an empirical inquiry. Annals of Operations Research , 1–30. Early Access

Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021a). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163 , 120447.

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2021b). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research , 1–26.

Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165 ,

Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3 ,

Bonsón, E., Lavorato, D., Lamboglia, R., & Mancini, D. (2021). Artificial intelligence activities and ethical approaches in leading listed companies in the European Union. International Journal of Accounting Information Systems, 43 ,

Boobier, T. (2018). Advanced analytics and AI: Impact, implementation, and the future of work (p. 304). John Wiley & Sons.

Book   Google Scholar  

Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35 (1), 170–180.

Burgos, D., & Ivanov, D. (2021). Food retail supply chain resilience and the COVID-19 pandemic: Adigital twin-based impact analysis and improvement directions. Transportation Research Part E: Logistics and Transportation Review, 152 , 102412.

Butt, A. S. (2021). Mitigating the effects of COVID-19: An exploratory case study of the countermeasures taken by the manufacturing industry. Journal of Business & Industrial Marketing . Early Access

Caballero-Morales, S. O. (2021). Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic. Research in International Business and Finance, 57 ,

Cadden, T., Dennehy, D., Mantymaki, M., & Treacy, R. (2022). Understanding the influential and mediating role of cultural enablers of AI integration to supply chain. International Journal of Production Research, 60 (14), 4592–4620.

Carvalho, H., Naghshineh, B., Govindan, K., & Cruz-Machado, V. (2022). The resilience of on-time delivery to capacity and material shortages: An empirical investigation in the automotive supply chain. Computers & Industrial Engineering, 171 ,

Chanchaichujit, J., Balasubramanian, S., & Charmaine, N. S. M. (2020). A systematic literature review on the benefit-drivers of RFID implementation in supply chains and its impact on organizational competitive advantage. Cogent Business & Management, 7 (1), 1818408.

Chander, B., Pal, S., De, D., & Buyya, R. (2022). Artificial intelligence-based internet of things for industry 5.0. In S. Pal, D. De, & R. Buyya (Eds.), Artificial Intelligence-based Internet of Things Systems (pp. 3–45). Internet of things

Chari, A., Niedenzu, D., Despeisse, M., Machado, C. G., Azevedo, J. D., Boavida-Dias, R., & Johansson, B. (2022). Dynamic capabilities for circular manufacturing supply chains—Exploring the role of Industry 4.0 and resilience. Business Strategy and the Environment, 31 (5), 2500–2517.

Chatterjee, P. (2021). Analytics in the age of artificial intelligence: The why and the how of using analytics to unleash the power of artificial intelligence . Atlantic Publishing Company.

Chen, C. L. (2019). Value creation by SMEs participating in global value chains under industry 4.0 trend: Case study of textile industry in Taiwan. Journal of Global Information Technology Management, 22 (2), 120–145.

Chen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2021). Artificial intelligence-based human-centric decision support framework: An application to predictive maintenance in asset management under pandemic environments. Annals of Operations Research , 1–24. Early Access

Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: Understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29 (6), 613–635.

Chowdhury, P., Paul, S. K., Kaisar, S., & Moktadir, M. A. (2021). COVID-19 pandemic related supply chainstudies: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 148 , 102271.

Chryssolouris, G., Alexopoulos, K., & Arkouli, Z. (2023). Artificial intelligence in manufacturing systems. A Perspective on Artificial Intelligence in Manufacturing (pp. 79–135). Springer International Publishing.

Chapter   Google Scholar  

Chung, S. H. (2021). Applications of smart technologies in logistics and transport: A review. Transportation Research Part e: Logistics and Transportation Review, 153 ,

Clulow, V., Gerstman, J., & Barry, C. (2003). The resource-based view and sustainable competitive advantage: The case of a financial services firm. Journal of European Industrial Training, 27 (5), 220–232.

Cugno, M., Castagnoli, R., & Büchi, G. (2021). Openness to Industry 4.0 and performance: The impact of barriers and incentives. Technological Forecasting and Social Change, 168 , 120756.

Cui, L., Wu, H., & Dai, J. (2023). Modelling flexible decisions about sustainable supplier selection in multitier sustainable supply chain management. International Journal of Production Research, 61 (14), 4603–4624.

de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: A framework for governance. Ethics and Information Technology, 23 (3), 505–525.

Dash Wu, D. (2020). Data intelligence and risk analytics. Industrial Management & Data Systems, 120 (2), 249–252.

Delic, M., & Eyers, D. R. (2020). The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry. International Journal of Production Economics, 228 ,

Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121 , 283–314.

Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy artificial intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99 ,

Doyle-Kent, M., & Kopacek, P. (2020). Industry 5.0: Is the manufacturing industry on the cusp of a new revolution? In N. Durakbasa & M. Gençyılmaz (Eds.), Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. Lecture Notes in Mechanical Engineering (pp. 432–441)

Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250 ,

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., & Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57 ,

Eichengreen, B. (2023). Financial regulation in the age of the platform economy. Journal of Banking Regulation, 24 (1), 40–50.

Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25 (2), 119–142.

Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26 (6), 3333–3361.

Feng, C., Patel, P. C., & Fay, S. (2021). The value of the structural power of the chief information officer in enhancing forward-looking firm performance. Journal of Management Information Systems, 38 (3), 765–797.

Frederico, G. F. (2021). From Supply Chain 4.0 to Supply Chain 5.0: Findings from a Systematic Literature Review and Research Directions. Logistics, 5(3).

Frederico, G. F., Kumar, V., Garza-Reyes, J. A., Kumar, A., & Agrawal, R. (2023). Impact of I4.0 technologies and their interoperability on performance: Future pathways for supply chain resilience post-COVID-19. The International Journal of Logistics Management, 34 (4), 1020–1049.

Ganesh, N. V., Kalaivanan, R., Al-Mdallal, Q. M., & Reena, K. (2021). Buoyancy driven second grade nano boundary layers over a catalytic surface with reaction rate, heat of reaction and activation energy at boundary. Case Studies in Thermal Engineering, 28 ,

Ghazali, I., Abdul-Rashid, S. H., Md Dawal, S. Z., Aoyama, H., Sakundarini, N., Ho, F. H., & Herawan, S. G. (2021). Green product preferences considering cultural influences: A comparison study between Malaysia and Indonesia. Management of Environmental Quality: An International Journal, 32 (5), 1040–1063.

Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tirkolaee, E. B., Asadi, S., & Amran, A. (2023a). Industry 5.0 implications for inclusive sustainable manufacturing: An evidence-knowledge-based strategic roadmap. Journal of Cleaner Production, 417 , 138023.

Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021). Industry 4.0, innovation, and sustainable development: A systematic review and a roadmap to sustainable innovation. Business Strategy and the Environment, 30 (8), 4237–4257.

Ghobakhloo, M., Iranmanesh, M., Mubarak, M. F., Mubarik, M., Rejeb, A., & Nilashi, M. (2022). Identifying industry 5.0 contributions to sustainable development: A strategy roadmap for delivering sustainability values. Sustainable Production and Consumption, 33 , 716–737.

Ghobakhloo, M., Iranmanesh, M., Tseng, M. L., Grybauskas, A., Stefanini, A., & Amran, A. (2023b). Behind the definition of Industry 5.0: A systematic review of technologies, principles, components, and values. Journal of Industrial and Production Engineering , 1–16.

Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3 (4), 208–218.

Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supplychain modeling: systematic literature review in the context of the COVID-19 pandemic. EnvironmentSystems and Decisions, 40 (2), 222–243.

Gordon, A. V., Ramic, M., Rohrbeck, R., & Spaniol, M. J. (2020). 50 Years of corporate and organizational foresight: Looking back and going forward. Technological Forecasting and Social Change, 154 ,

Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023). The role of artificial intelligence in the procurement process: State of the art and research agenda. Journal of Purchasing and Supply Management, 29 (2), 100823.

Guo, K., Wu, M., Soo, Z., Yang, Y., Zhang, Y., Zhang, Q., ... & Lu, J. (2023). Artificial intelligence-driven biomedical genomics. Knowledge-Based Systems, 279 , 110937.

Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations & Production Management, 28 (10), 991–1012.

Hannan, M. A., Begum, R. A., Al-Shetwi, A. Q., Ker, P. J., Al Mamun, M. A., & Hussain, A.,... & Mahlia, T. M. I. (2020). Waste collection route optimisation model for linking cost saving and emission reduction to achieve sustainable development goals. Sustainable Cities and Society, 62 ,

Harima, A. (2022). Transnational migration entrepreneurship during a crisis: Immediate response to challenges and opportunities emerging through the COVID-19 pandemic. Business and Society Review, 127 , 223–251.

Harjoto, M. A., & Rossi, F. (2023). Market reaction to the COVID-19 pandemic: evidence from emergingmarkets. International Journal of Emerging Markets, 18 (1), 173–199.

He, L., Xue, M., & Gu, B. (2020). Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications. Journal of Management Science and Engineering, 5 (1), 1–22.

Hong, J., Liao, Y., Zhang, Y., & Yu, Z. (2019). The effect of supply chain quality management practices and capabilities on operational and innovation performance: Evidence from Chinese manufacturers. International Journal of Production Economics, 212 , 227–235.

Hua, S. S., & Belfield, H. (2020). AI & antitrust: Reconciling tensions between competition law and cooperative AI development. Yale Journal of Law & Technology, 23 , 415.

Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49 , 30–50.

Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32 (9), 775–788.

Jamrus, T., Wang, H. K., & Chien, C. F. (2020). Dynamic coordinated scheduling for supply chain under uncertain production time to empower smart production for Industry 3.5. Computers & Industrial Engineering, 142 , 106375.

Jauhar, S. K., Jani, S. M., Kamble, S. S., Pratap, S., Belhadi, A., & Gupta, S. (2023). How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains. International Journal of Production Research , 1–25. Early Access

Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2022). Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable Operations and Computers, 3 , 203–217.

Jazairy, A., & von Haartman, R. (2020). Analysing the institutional pressures on shippers and logistics service providers to implement green supply chain management practices. International Journal of Logistics Research and Applications, 23 (1), 44–84.

Jiang, P., Van Fan, Y., & Klemeš, J. J. (2021). Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities. Applied Energy, 285 ,

Jiang, Z., Wen, H., Han, F., Tang, Y., & Xiong, Y. (2022). Data-driven generative design for mass customization: A case study. Advanced Engineering Informatics, 54 ,

Jiao, S., Li, W., & Wen, J. (2022). Spatiotemporal changes of manufacturing firms in the flood prone Yangtze Delta. Environmental Hazards, 21 (4), 334–360.

Kabir, M. N., & Kabir, M. N. (2019). Strategy, strategy formulation, and business models. Knowledge-Based Social Entrepreneurship. Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth. New York: Palgrave Macmillan.

Kamyab, H., Khademi, T., Chelliapan, S., SaberiKamarposhti, M., Rezania, S., Yusuf, M., . . . & Ahn, Y. (2023). The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results in Engineering, 20 , 101566.

Kang, J., Diao, Z., & Zanini, M. T. (2021). Business-to-business marketing responses to COVID-19 crisis: A business process perspective. Marketing Intelligence & Planning, 39 (3), 454–468.

Karmaker, C. L., Ahmed, T., Ahmed, S., Ali, S. M., Moktadir, M. A., & Kabir, G. (2021). Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model. Sustainable Production and Consumption, 26 , 411–427.

Khatua, P. K., Ramachandaramurthy, V. K., Kasinathan, P., Yong, J. Y., Pasupuleti, J., & Rajagopalan, A. (2020). Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues. Sustainable Cities and Society, 53 ,

Kumar, A., & Nayyar, A. (2020). si3-Industry: A sustainable, intelligent, innovative, internet-of-things industry. In A. Nayyar & A. Kumar (Eds.), A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development. Advances in Science, Technology & Innovation (pp. 1–21)

Kurpjuweit, S., Schmidt, C. G., Klöckner, M., & Wagner, S. M. (2021). Blockchain in AdditiveManufacturing and its Impact on Supply Chains. Journal of Business Logistics, 42 (1), 46–70.

Le Bot, C., Perrigot, R., & Cliquet, G. (2023). Franchise vs. independent retail and service stores: customer perceptions. In G. W. Hendrikse, G. Cliquet, I. Hajdini, A. Raha, & J. Windsperger (Eds.), Networks in International Business Contributions to Management Science (pp. 171–200)

Lee, C. T., Pan, L. Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: A two-stage structural equation modeling-artificial neural network approach. Internet Research, 32 (4), 1329–1356.

Lei, Z., Cui, L., Tang, J., Chen, L., & Liu, B. (2023). Supply chain resilience in the context of I4.0 and I5.0 from a multilayer network ripple effect perspective. Annals of Operations Research , 1–44.Early Access

Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., & Liu, Q., ... & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65 , 279–295.

Leng, J., Zhong, Y., Lin, Z., Xu, K., Mourtzis, D., & Zhou, X.,... & Shen, W. (2023). Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm. Journal of Manufacturing Systems, 71 , 95–114.

Li, G., Yuan, C., Kamarthi, S., Moghaddam, M., & Jin, X. (2021). Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. Journal of Manufacturing Systems, 60 , 692–706.

Li, H., Yang, X., Wang, F., Rose, T., Chan, G., & Dong, S. (2016). Stochastic state sequence model to predict construction site safety states through Real-Time Location Systems. Safety Science, 84 , 78–87.

Liu, J., Chang, H., Forrest, J. Y. L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of China's manufacturing sectors. Technological Forecasting and Social Change, 158 ,

Liu, P., Hendalianpour, A., Hamzehlou, M., & Feylizadeh, M. (2022). Cost reduction of inventory-production-system in multi-echelon supply chain using game theory and fuzzy demand forecasting. International Journal of Fuzzy Systems, 24 (4), 1793–1813.

Lynch, P., Hasbrouck, C. R., Wilck, J., Kay, M., & Manogharan, G. (2020). Challenges and opportunities to integrate the oldest and newest manufacturing processes: metal casting and additive manufacturing. Rapid Prototyping Journal, 26 (6), 1145–1154.

Maddikunta, P. K. R., Pham, Q. V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T. R., & Liyanage, M. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26 , 100257.

Magableh, G. M. (2021). Supply chains and the COVID-19 pandemic: A comprehensive framework. European Management Review, 18 (3), 363–382.

Magazzino, C., Mele, M., & Schneider, N. (2022). A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation. Structural Change and Economic Dynamics, 60 , 315–328.

Majumdar, A., Shaw, M., & Sinha, S. K. (2020). COVID-19 debunks the myth of socially sustainablesupply chain: A case of the clothing industry in South Asian countries. Sustainable Production andConsumption, 24 , 150–155.

Malik, A., Budhwar, P., & Srikanth, N. R. (2020). Gig economy, 4IR and artificial intelligence: Rethinking strategic HRM. Human & technological resource management (HTRM): New insights into revolution 4.0 ,75–88.

Mantelero, A. (2018). AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. Computer Law & Security Review, 34 (4), 754–772.

Manuel Maqueira, J., Moyano-Fuentes, J., & Bruque, S. (2019). Drivers and consequences of aninnovative technology assimilation in the supply chain: cloud computing and supply chain integration. International Journal of Production Research, 57 (7), 2083–2103.

Marr, B. (2021). Data strategy: How to profit from a world of big data, analytics and artificial intelligence (p. 272). Kogan Page Publishers.

Marsden, T. (2013). From post-productionism to reflexive governance: Contested transitions in securing more sustainable food futures. Journal of Rural Studies, 29 , 123–134.

Meherishi, L., Narayana, S. A., & Ranjani, K. S. (2019). Sustainable packaging for supply chain management in the circular economy: A review. Journal of Cleaner Production, 237 ,

Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next? Omega, 87 , 205–225.

Miriyala, S. S., Subramanian, V. R., & Mitra, K. (2018). TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study. European Journal of Operational Research, 264 (1), 294–309.

Mithas, S., Chen, Z. L., Saldanha, T. J., & De Oliveira Silveira, A. (2022). How will artificial intelligence and Industry 4.0 emerging technologies transform operations management? Production and Operations Management, 31 (12), 4475–4487.

Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management, 33 (4), 1246–1268.

Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decisionmaking model. Omega, 96 , 102075.

Moktadir, M. A., Dwivedi, A., Khan, N. S., Paul, S. K., Khan, S. A., Ahmed, S., & Sultana, R. (2021). Analysis of risk factors in sustainable supply chain management in an emerging economy of leather industry. Journal of Cleaner Production, 283 ,

Mostafiz, M. I., Musteen, M., Saiyed, A., & Ahsan, M. (2022). COVID-19 and the global value chain: Immediate dynamics and long-term restructuring in the garment industry. Journal of Business Research, 139 , 1588–1603.

Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment, 31 (5), 2068–2081.

Nandi, S., Sarkis, J., Hervani, A., & Helms, M. (2021). Do blockchain and circular economy practices improve post COVID-19 supply chains? A resource-based and resource dependence perspective. Industrial Management & Data Systems, 121 (2), 333–363.

Nayeri, S., Sazvar, Z., & Heydari, J. (2023). Designing an IoT-enabled supply chain network considering the perspective of the Fifth Industrial Revolution: Application in the medical devices industry. Engineering Applications of Artificial Intelligence, 122 ,

Naz, F., Agrawal, R., Kumar, A., Gunasekaran, A., Majumdar, A., & Luthra, S. (2022). Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions. Business Strategy and the Environment, 31 (5), 2400–2423.

O’Dwyer, M., Filieri, R., & O’Malley, L. (2023). Establishing successful university–industry collaborations: Barriers and enablers deconstructed. The Journal of Technology Transfer, 48 (3), 900–931.

Okano-Heijmans, M., & Vosse, W. (2021). Promoting open and inclusive connectivity: The case for digital development cooperation. Research in Globalization, 3 ,

O’Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., . . . Ashrafian, H. (2019). Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. The International Journal of Medical Robotics and Computer Assisted Surgery, 15 (1), e1968.

Palanisamy, M., Pugalendhi, A., & Ranganathan, R. (2020). Selection of suitable additive manufacturing machine and materials through best–worst method (BWM). The International Journal of Advanced Manufacturing Technology, 107 , 2345–2362.

Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8 , 220121–220139.

Pessot, E., Zangiacomi, A., & Fornasiero, R. (2022). Unboxing the hyper-connected supply chain: A case study in the furniture industry. Production Planning & Control, 35 (6), 580–598.

Pivoto, D. G., de Almeida, L. F., da Rosa Righi, R., Rodrigues, J. J., Lugli, A. B., & Alberti, A. M. (2021). Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review. Journal of Manufacturing Systems, 58 , 176–192.

Polyviou, M., Croxton, K. L., & Knemeyer, A. M. (2020). Resilience of medium-sized firms to supply chain disruptions: The role of internal social capital. International Journal of Operations & Production Management, 40 (1), 68–91.

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241 ,

Pramanik, P. K. D., Pal, S., & Choudhury, P. (2018). Beyond automation: The cognitive IoT. Artificial intelligence brings sense to the Internet of Things. Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Applications, 14 , 1–37.

Psarommatis, F., May, G., & Azamfirei, V. (2023). Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems, 68 , 376–399.

Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8 (1), 45.

Riahi, Y., Saikouk, T., Badraoui, I., & Fosso Wamba, S. (2023). Researched topics, patterns, barriers and enablers of artificial intelligence implementation in supply chain: A Latent-Dirichlet-allocation-based topic-modelling and expert validation. Production Planning & Control , 1–28. Early Access

Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173 ,

Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33 (1)

Roy, S., Das, M., Ali, S. M., Raihan, A. S., Paul, S. K., & Kabir, G. (2020). Evaluating strategies for environmental sustainability in a supply chain of an emerging economy. Journal of Cleaner Production, 262 ,

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54 (5), 758–790.

Sahu, C. K., Young, C., & Rai, R. (2021). Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: A review. International Journal of Production Research, 59 (16), 4903–4959.

Salam, M. A. (2021). Analyzing manufacturing strategies and Industry 4.0 supplier performance relationships from a resource-based perspective. Benchmarking: An International Journal, 28 (5), 1697–1716.

Salkin, C., Oner, M., Ustundag, A., & Cevikcan, E. (2018). A conceptual framework for industry 4.0. In Industry 4.0: Managing The Digital Transformation (pp. 3–23). Springer Series in Advanced Manufacturing.

Samadhiya, A., Kumar, A., Yadav, S., Luthra, S., Jabbour, C. J. C., & Agrawal, R. (2023). Artificial intelligence-partner relationships management for climate management in B2B firms to achieve sustainable competitiveness. Industrial Marketing Management, 115 , 510–525.

Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021). Ai-driven cybersecurity: An overview, security intelligence modeling and research directions. SN Computer Science, 2 , 1–18.

Schmitt, M. (2023). Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection. Journal of Industrial Information Integration, 36 ,

Seuring, S., Sarkis, J., Müller, M., & Rao, P. (2008). Sustainability and supply chain management–An introduction to the special issue. Journal of Cleaner Production, 16 (15), 1545–1551.

Sharma, L., & Garg, P. K. (Eds.). (2023). Technological prospects and social applications of society 5.0 (p. 278). CRC Press.

Sheng, J., Amankwah-Amoah, J., Khan, Z., & Wang, X. (2021). COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32 (4), 1164–1183.

Sousa, M. J., & Wilks, D. (2018). Sustainable skills for the world of work in the digital age. Systems Research and Behavioral Science, 35 (4), 399–405.

Srivastava, S. K., Goel, P., Anisha, & Sindhu, S. (2022). Industry 5.0: Coexistence of humans and machines. Decision Support Systems for Smart City Applications , 137–152.

Thakur, R., Borkar, P. S., & Agarwal, M. (2022). Smart Society 5.0 for social and technological sustainability. Decision Analytics for Sustainable Development in Smart Society 5.0: Issues, Challenges and Opportunities (pp. 299–319). Springer Nature Singapore: Singapore.

Thapa, C., & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129 ,

Thurner, S., Hanel, R., & Klimek, P. (2018). Introduction to the theory of complex systems (p. 480). Oxford University Press.

Titumir, R. A. M., & Titumir, R. A. M. (2021). Manufacturing Industry of Bangladesh. Numbers and Narratives in Bangladesh's Economic Development , 63–95.

Tsang, Y. P., Choy, K. L., Wu, C. H., Ho, G. T., Lam, C. H., & Koo, P. S. (2018). An Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks. Industrial Management & Data Systems, 118 (7), 1432–1462.

Unhelkar, B., Joshi, S., Sharma, M., Prakash, S., Mani, A. K., & Prasad, M. (2022). Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review. International Journal of Information Management Data Insights, 2 (2), 100084.

Vasili, M. R., Tang, S. H., & Vasili, M. (2012). Automated storage and retrieval systems: A review on travel time models and control policies. Warehousing in the Global Supply Chain: Advanced Models, Tools and Applications for Storage Systems , 159–209.

Villar, A., Paladini, S., & Buckley, O. (2023). Towards supply chain 5.0: Redesigning supply chains as resilient, sustainable, and human-centric systems in a post-pandemic world. Operations Research Forum, 4 , 60.

Vincent, V. U. (2021). Integrating intuition and artificial intelligence in organizational decision-making. Business Horizons, 64 (4), 425–438.

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26 (7), 1893–1924.

Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y., & Freiheit, T. (2021). Smart manufacturing and intelligent manufacturing: A comparative review. Engineering, 7 (6), 738–757.

Yaacoub, J. P. A., Noura, H. N., Salman, O., & Chehab, A. (2023). Ethical hacking for IoT: Security issues, challenges, solutions and recommendations. Internet of Things and Cyber-Physical Systems, 3 , 280–308.

Yadav, S., Luthra, S., & Garg, D. (2021). Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environmental Science and Pollution Research, 28 , 16633–16654.

Yalcin Kavus, B., Ayyildiz, E., Gulum Tas, P., & Taskin, A. (2023). A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem. Environmental Science and Pollution Research, 30 (39), 90006–90023.

Zhang, H., Gao, H., & Liu, P. (2023). Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method. Complex & Intelligent Systems, 9 (4), 4025–4060.

Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., . . . Burke, A. F. (2023). Autonomous driving system: A comprehensive survey. Expert Systems with Applications , 122836.

Zhou, Y., Soh, Y. S., Loh, H. S., & Yuen, K. F. (2020). The key challenges and critical success factors of blockchain implementation: Policy implications for Singapore’s maritime industry. Marine Policy, 122 ,

Zsidisin, G. A., Melnyk, S. A., & Ragatz, G. L. (2005). An institutional theory perspective of business continuity planning for purchasing and supply management. International Journal of Production Research, 43 (16), 3401–3420.

Download references

This work was supported by the 19th Batch of Innovative and Entrepreneurial Talent Funding Projects in Jilin Province; Jilin Province Excellent Youth Scientific Research and Innovation Talents Project in Universities; Jilin Province Science and Technology Development Plan Project: Research on Evaluation and Improvement of Technological Innovation Efficiency of Industrial Enterprises in Jilin Province; Jilin Province Educational Science Planning Project: Research on the Improvement and Construction of Inspection Mechanism of Education System in Jilin Province; and Jilin University of Finance and Economics Think Tank Cultivation Project: Research on the Development of Chemical Fiber Industry in Jilin Province.

Author information

Authors and affiliations.

College of Accounting, Jilin University of Finance and Economics, Changchun, 130117, China

Haoyang Wu, Jing Liu & Biming Liang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Haoyang Wu .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Wu, H., Liu, J. & Liang, B. AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01999-6

Download citation

Received : 03 January 2024

Accepted : 15 April 2024

Published : 13 June 2024

DOI : https://doi.org/10.1007/s13132-024-01999-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Intelligent supply chain management (SCs)
• Artificial intelligence (AI)
• Industry 5.0 (I5.0)
• Best-worst method (BWM)
• Internet of Things (IoT)
• Find a journal
• Publish with us
• Track your research