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Pandemic Triggered Emergency Supply Chain Management Innovations: A Scientometric Analysis Based on Bibliometrics and Dynamic Topic Models

Published online by Cambridge University Press:  11 April 2025

Tian Xie Open the ORCID record for Tian Xie [Opens in a new window] ,
Gui-Ye Dai ,
Wei-Fan Chen ,
Chen-Peng Yang ,
Yong-Jian Huang  and
Yao-Yao Wei Open the ORCID record for Yao-Yao Wei [Opens in a new window]
Tian Xie
Affiliation:
School of Economics, Management and Law, University of South China, Hunan, PR China Information Sciences and Technology at The Pennsylvania State University, University Park, PA, USA
Gui-Ye Dai
Affiliation:
School of Economics, Management and Law, University of South China, Hunan, PR China
Wei-Fan Chen
Affiliation:
Information Sciences and Technology at The Pennsylvania State University, University Park, PA, USA
Chen-Peng Yang
Affiliation:
School of Economics, Management and Law, University of South China, Hunan, PR China
Yong-Jian Huang
Affiliation:
School Management, Shanghai University, Shanghai, PR China
Yao-Yao Wei*
Affiliation:
School of Economics, Management and Law, University of South China, Hunan, PR China Information Sciences and Technology at The Pennsylvania State University, University Park, PA, USA School of Education at Central China Normal University, Hubei, PR China
*
Corresponding author: Yao-Yao Wei; Email: [email protected]
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Abstract

Objective

The outbreak of major epidemics, such as COVID-19, has had a significant impact on supply chains. This study aimed to explore knowledge innovation in the field of emergency supply chain during pandemics with a systematic quantitative analysis.

Methods

Based on the Web of Science (WOS) Core Collection, proposing a 3-stage systematic analysis framework, and utilizing bibliometrics, Dynamic Topic Models (DTM), and regression analysis to comprehensively examine supply chain innovations triggered by pandemics.

Results

A total of 888 literature were obtained from the WOS database. There was a surge in the number of publications in recent years, indicating a new field of research on Pandemic Triggered Emergency Supply Chain (PTESC) is gradually forming. Through a 3-stage analysis, this study identifies the literature knowledge base and distribution of research hotspots in this field and predicts future research hotspots and trends mainly boil down to 3 aspects: pandemic-triggered emergency supply chain innovations in key industries, management, and technologies.

Conclusions

COVID-19 strengthened academic exchange and cooperation and promoted knowledge output in this field. This study provides an in-depth perspective on emergency supply chain research and helps researchers understand the overall landscape of the field, identifying future research directions.

Keywords

pandemicemergency supply chainbibliometricsDTM
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 19 , 2025 , e88
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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References

Aday, S, Aday, MS. Impact of COVID-19 on the food supply chain. Food Qual Saf. 2020;4(4):167–180CrossRefGoogle Scholar
Duijzer, LE, van Jaarsveld, W, Dekker, R. Literature review: the vaccine supply chain. Eur J Oper Res. 2018;268(1):174–192CrossRefGoogle Scholar
Spieske, A, Birkel, H. Improving supply chain resilience through industry 4.0: a systematic literature review under the impressions of the COVID-19 pandemic. Comput Ind Eng. 2021;158:107452. doi:10.1016/j.cie.2021.107452CrossRefGoogle ScholarPubMed
Rinaldi, M, Murino, T, Gebennini, E, et al. A literature review on quantitative models for supply chain risk management: can they be applied to pandemic disruptions? Comput Ind Eng. 2022;170:108329. doi:10.1016/j.cie.2022.108329CrossRefGoogle ScholarPubMed
Naz, F, Kumar, A, Majumdar, A, et al. Is artificial intelligence an enabler of supply chain resiliency post COVID-19? an exploratory state-of-the-art review for future research. Oper Manag Res. 2022;15(1):378–398. doi:10.1077/s12063-021-00208-wCrossRefGoogle Scholar
Modgil, S, Gupta, S, Stekelorum, R, et al. AI technologies and their impact on supply chain resilience during COVID-19. Int J Phys Distrib Logist Manag. 2021;52(2):130–149. doi:10.1108/IJPDLM-12-2020-0434CrossRefGoogle Scholar
Chowdhury, P, Paul, SK, Kaisar, S, et al. COVID-19 pandemic related supply chain studies: a systematic review. Transp Res E Logist Transp Rev. 2021;148: 102271. doi:10.1016/j.tre.2021.102271CrossRefGoogle ScholarPubMed
Shi, X, Liu, W, Zhang, J. Present and future trends of supply chain management in the presence of COVID-19: a structured literature review. Int J Logist Res Appl. 2021;26(7):813–842. doi:10.1080/13675567.2021.1988909CrossRefGoogle Scholar
Cordeiro, MC, Santos, L, Angelo, ACM, et al. Research directions for supply chain management in facing pandemics: an assessment based on bibliometric analysis and systematic literature review. Int J Logist Res Appl. 2021;25 (10):1313–1333. doi:10.1080/13675567.2021.1902487CrossRefGoogle Scholar
Rodriguez, A, Laio, A. Clustering by fast search and find of density peaks. Science. 2014;344(6191):1492–1496. doi:10.1126/science.1242072CrossRefGoogle ScholarPubMed
Chen, C. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol. 2005;57(3):359–377. doi:10.1002/asi.20317CrossRefGoogle Scholar
Blei, DM, Lafferty, JD. Dynamic topic models. Paper presented at: 23rd International Conference on Machine Learning; June. 2006. doi: 10.1145/1143844.1143859CrossRefGoogle Scholar
Blei, DM, Ng, AY, Jordan, MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993–1022Google Scholar
Röder, M, Both, A, Hinneburg, A. Exploring the space of topic coherence measures. Paper presented at: 8th ACM International Conference on Web Search and Data Mining; February 2-6, 2015. doi: 10.1145/2684822.2685324.CrossRefGoogle Scholar
Hobbs, JE. Food supply chains during the COVID-19 pandemic. Can J Agric Econ. 2020;68(2):171–176. doi:10.1111/cjag.12237CrossRefGoogle Scholar
Alam, ST, Ahmed, S, Ali, SM, et al. Challenges to COVID-19 vaccine supply chain: implications for sustainable development goals. Int J Prod Econ. 2021;239:108193. doi:10.1016/j.ijpe.2021.108193CrossRefGoogle ScholarPubMed
Branch-Elliman, W, Price, CS, Bessesen, MT, et al. Using the pillars of infection prevention to build an effective program for reducing the transmission of emerging and reemerging infections. Curr Environ Health Rep. 2015;2:226–235. doi:10.1007/s40572-015-0059-7CrossRefGoogle ScholarPubMed
Ivanov, D. Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann Oper Res. 2022;319(1):1411–1431. doi:10.1007/s10479-020-03640-6CrossRefGoogle ScholarPubMed
Pettit, TJ, Croxton, KL, Fiksel, J. The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience. J Bus Logist. 2019;40(1):56–65. doi:10.1111/jbl.12202CrossRefGoogle Scholar
Sarkis, J. Supply chain sustainability: learning from the COVID-19 pandemic. Int J Oper Prod Manag. 2020;41(1):63–73. doi:10.1108/IJOPM-08-2020-0568.CrossRefGoogle Scholar
Dubey, R, Gunasekaran, A, Bryde, DJ, et al. Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. Int J Prod Res. 2020;58(11):3381–3398. doi:10.1080/00207543.2020.1722860CrossRefGoogle 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. Int J Prod Res. 2018;57(3):829–846. doi:10.1080/00207543.2018.1488086CrossRefGoogle Scholar
Ivanov, D. Transformation of supply chain resilience research through the COVID-19 pandemic. Int J Prod Res. 2024;62(23):8217–8238. doi:10.1080/00207543.2024.2334420CrossRefGoogle Scholar
Furek, A, Edirisooriya, M, Casey, M, et al. Using the number of N95® filtering facepiece respirator models as an indicator of supply chain stability in a US health-care system. Disaster Med Public Health Prep. 2024;18:e10. doi:10.1017/dmp.2024.9CrossRefGoogle Scholar
Yadav, S, Kumar Mangla, S, Priyamvada, P, et al. An energy-efficient model for PPE waste management in a closed-loop supply chain. Bus Strateg Environ. 2023;33(2):1191–1207. doi:10.1002/bse.3541CrossRefGoogle Scholar
Vaupel, F, Fengler, I, Mutters, NT, et al. Investigation of three different UV-C irradiation schemes for bacterial decontamination of FFP2 masks to make them reusable. Disaster Med Public Health Prep. 2024;18:e91. doi:10.1017/dmp.2024.86CrossRefGoogle ScholarPubMed
Toigo, S, Jacques, M, Razek, T, et al. Fit testing retrofitted full-face snorkel masks as a form of novel personal protective equipment during the COVID-19 pandemic. Disaster Med Public Health Prep. 2021;1–16. doi:10.1017/dmp.2021.133Google ScholarPubMed
Sigala, IF, Sirenko, M, Comes, T, et al. Mitigating personal protective equipment (PPE) supply chain disruptions in pandemics–a system dynamics approach. Int J Oper Prod Manag. 2022;42(13):128–154. doi:10.1108/IJOPM-09-2021-0608CrossRefGoogle Scholar
Munasinghe, UJ, Halgamuge, MN. Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system. Expert Syst Appl. 2023;228:120293. doi:10.1016/j.eswa.2023.120293CrossRefGoogle ScholarPubMed
Hu, H, Xu, J, Liu, M, et al. Vaccine supply chain management: an intelligent system utilizing blockchain, IoT and machine learning. J Bus Res. 2023;156:113480. doi:10.1016/j.jbusres.2022.113480CrossRefGoogle ScholarPubMed
Ali, MH, Suleiman, N, Khalid, N, et al. Supply chain resilience reactive strategies for food SMEs in coping to COVID-19 crisis. Trends Food Sci Technol. 2021;109:94–102. doi:10.1016/j.tifs.2021.01.021CrossRefGoogle ScholarPubMed
Xu, X, Sethi, SP, Chung, SH, et al. Reforming global supply chain management under pandemics: the GREAT-3Rs framework. Prod Oper Manag. 2023;32(2):524–546. doi:10.1111/poms.13885CrossRefGoogle Scholar
Nasir, SB, Ahmed, T, Karmaker, CL, et al. Supply chain viability in the context of COVID-19 pandemic in small and medium-sized enterprises: implications for sustainable development goals. J Enterp Inf Manag. 2022;35(1):100–124. doi:10.1108/JEIM-02-2021-0091CrossRefGoogle Scholar
Rajak, S, Mathiyazhagan, K, Agarwal, V, et al. Issues and analysis of critical success factors for the sustainable initiatives in the supply chain during COVID-19 pandemic outbreak in India: a case study. Res Transp Econ. 2022;93:101114. doi:10.1016/j.retrec.2021.101114CrossRefGoogle Scholar
Kovács, G, Falagara Sigala, I. Lessons learned from humanitarian logistics to manage supply chain disruptions. J Supply Chain Manag. 2021;57(1):41–49. doi:10.1111/jscm.12253CrossRefGoogle Scholar
Bhaskar, S, Tan, J, Bogers, ML, et al. At the epicenter of COVID-19–the tragic failure of the global supply chain for medical supplies. Front Public Health. 2020;8:562882. doi:10.3389/fpubh.2020.562882CrossRefGoogle ScholarPubMed
Nandi, S, Sarkis, J, Hervani, AA, et al. Redesigning supply chains using blockchain-enabled circular economy and COVID-19 experiences. Sustain Prod Consum. 2021;27:10–22. doi:10.1016/j.spc.2020.10.019CrossRefGoogle ScholarPubMed
Dubey, R. Unleashing the potential of digital technologies in emergency supply chain: the moderating effect of crisis leadership. Ind Manag Data Syst. 2023; 123(1):112–132. doi:10.1108/IMDS-05-2022-0307CrossRefGoogle Scholar
Choi, T-M. Innovative “bring-service-near-your-home” operations under coronavirus (COVID-19/SARS-CoV-2) outbreak: can logistics become the messiah? Transp Res E Logist Transp Rev. 2020;140:101961. doi:10.1016/j.tre.2020.101961CrossRefGoogle ScholarPubMed
Joshi, S, Sharma, M, Luthra, S, et al. Role of industry 4.0 in augmenting endurability of agri-food supply chains amidst pandemic: organisation flexibility as a moderator. Oper Manag Res. 2024:1–15. doi:10.1007/s12063-023-00436-2Google Scholar
Rahbari, M, Arshadi Khamseh, A, Mohammadi, M. A novel robust probabilistic chance constrained programming and strategic analysis for agri-food closed-loop supply chain under pandemic crisis. Soft Comput. 2024;28(2):1179–1214. doi:10.1007/s00500-023-09156-yCrossRefGoogle Scholar
Seif, M, Yaghoubi, S, Khodoomi, MR. Optimization of food-energy-water-waste nexus in a sustainable food supply chain under the COVID-19 pandemic: a case study in Iran. Environ Dev Sustain. 2024;26(3):7163–7197. doi:10.1007/s10668-023-03004-7CrossRefGoogle Scholar
SA-s, Salari, Sazvar, Z. Designing a sustainable vaccine supply chain by considering demand substitution and value-added function during a pandemic outbreak. Comput Ind Eng. 2024;187:109826. doi:10.1016/j.cie.2023.109826Google Scholar
Torshizi, E, Bozorgi-Amiri, A, Sabouhi, F. Resilient and sustainable global COVID-19 vaccine supply chain design considering reverse logistics. Appl Soft Comput. 2024;151:111041. doi:10.1016/j.asoc.2023.111041CrossRefGoogle Scholar
Fernando, Y, Al-Madani, MHM, Shaharudin, MS. COVID-19 and global supply chain risks mitigation: systematic review using a scientometric technique. J Sci Technol Policy Manag. 2024;15(6):26. doi:10.1108/JSTPM-01-2022-0013CrossRefGoogle Scholar
Tiwari, M, Bryde, DJ, Stavropolou, F, et al. Modelling supply chain visibility, digital technologies, environmental dynamism and healthcare supply chain resilience: an organisation information processing theory perspective. Transp Res E Logist Transp Rev. 2024;188:103613. doi:10.1016/j.tre.2024.103613CrossRefGoogle Scholar
Fallahi, A, Anaraki, SAM, Mokhtari, H, et al. Blood plasma supply chain planning to respond COVID-19 pandemic: a case study. Environ Dev Sustain. 2022:1–52. doi: 10.1007/s10668-022-02793-7Google ScholarPubMed
Ivanov, D. Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability. Int J Prod Econ. 2023;263:108938. doi: 10.1016/j.ijpe.2023.108938CrossRefGoogle Scholar
Schleifenheimer, M, Ivanov, D. Pharmaceutical retail supply chain responses to the COVID-19 pandemic. Ann Oper Res. 2024;Feb:26. doi: 10.1007/s10479-024-05866-0Google Scholar
Ding, HH, Tian, JW, Yu, W, et al. The application of artificial intelligence and big data in the food industry. Foods. 2023;12(24):4511. doi:10.3390/foods12244511CrossRefGoogle ScholarPubMed
Musamih, A, Yaqoob, I, Salah, K, et al. Metaverse in healthcare: applications, challenges, and future directions. IEEE Consum Electron Mag. 2023;12(4):33–46. doi:10.1109/MCE.2022.3223522CrossRefGoogle 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. Transp Res E Logist Transp Rev. 2020;136:101922. doi:10.1016/j.tre.2020.101922CrossRefGoogle ScholarPubMed