Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Sample Creation
- Only papers published in international journals were retrieved, while other types of publications were not considered;
- Similarly, only papers written in English were considered.
- The paper’s metadata: authors, journal, bibliographic data, publication year, document title, and document type (article vs. review);
- The authors’ keywords;
- The publication option (traditional vs. open access);
- Funding information.
3.2. Descriptive Analyses
3.3. Keyword Analysis and Trend
- Authors often use slightly different terms to express the same concept. This is the case for singular or plural forms, British or American spelling of words, usage of capital letters/lower case letters, usage of hyphenation, or abbreviations (e.g., COVID vs. Coronavirus);
- Acronyms can sometimes be used as keywords instead of the full text.
- Well-established (‘core’), with high frequency and high persistence. They are expected to denote themes that have long been studied by many authors in the field;
- Intermittent, with low frequency and high persistence. Terms in this category denote themes that have been known for many years, but have been studied with low continuity;
- Phantom/emerging, with low frequency and low persistence. These topics could be relatively new to the research field or could describe themes that have progressively disappeared;
- Trendy, with high frequency and low persistence. These topics are relatively new but have already attracted the attention of many researchers.
- The subset of keywords that were observed in all periods of analysis, as these terms are expected to reflect relevant themes to the selected field of research. For those terms, their classification across the four periods was mapped, so as to delineate a trend in the interest toward the specific theme;
- The subset of keywords with a minimum frequency of 30, thus indicating a high recurrence of the related topics in the targeted field of research. These keywords were first grouped by macro-areas; then, their trend in time was evaluated jointly with that of some core topics of the targeted field of research to identify possible correlations.
4. Results
4.1. Descriptive Analyses
4.2. Keyword Analysis and Trend
- (a)
- Keywords semantically related to the area of supply chain disruption (21 terms), namely resilience; supply chain disruption; disruption; risk management; supply chain risk management; supply disruption; supply chain risk; disruption management; uncertainty; risk; robustness; demand disruption; supply risk; disaster; vulnerability; risk assessment; supply chain vulnerabilities; transportation disruption; supply uncertainty; terrorism; supply risk management;
- (b)
- Keywords not semantically related to the topic of supply chain disruption but related to more general themes of supply chain or supply chain management (25 terms). These keywords include supply chain; supply chain management; logistics; closed-loop supply chain; supply chain design; supply chain network; global supply chain; supply chain coordination; inventory management; dual sourcing; information sharing; inventory; coordination; bullwhip effect; purchasing; visibility; business continuity planning; sourcing strategy; security; contingency planning; sourcing; backup supplier; safety stock; supply chain planning; and supply management;
- (c)
- Keywords not related to supply chain disruptions nor necessarily linked to supply chain topics (23 terms), namely the following: simulation; game theory; optimization; stochastic programming; case study; innovation; agility; analytic hierarchy process; flexibility; modeling; revenue sharing contract; agent-based model; empirical research; service level; quantity discount; asymmetric information; coordination mechanism; dynamic programming; radio frequency identification; buyback contract; Petri net; integration; and contract.
- Query-related terms: as the query settings expressively included terms such as “supply chain” and “disruption”, these terms (and their combination “supply chain disruption”) were grouped in a single query-related category;
- COVID-relates terms: this category includes the terms “COVID-19” and “COVID-19 pandemic”;
- Disruption-related terms: these terms are semantically related to the topic of “disruption”, which, however, is not necessarily used as a keyword. Those terms are supply disruption; pandemic; disruption risk; disruption management; uncertainty; ripple effect; demand disruption; and disaster;
- Risk- or resilience-related terms: this category includes terms that were not used in the query settings but that appear to be related to the more general theme of risk management or resilience, whose relationship with supply chain disruptions is obvious. These terms include (supply chain) resilience, (supply chain) risk management, (supply chain) risk, robustness; resilient supply chain, supply risk, reliability, risk assessment; or vulnerability;
- Supply chain-related terms: as per the classification made previously, these terms do not strictly refer to disruptions, but to more general problems in the area of supply chain or supply chain management. These terms include supply chain management, supplier selection, logistics, supply chain design, supply chain network, global supply chain, supply chain network design, collaboration, supply chain coordination, or inventory management;
- Sustainability-related terms: the sustainability perspective includes four terms, namely sustainability, closed-loop supply chain, climate change, and circular economy;
- Technology-related terms: this category includes terms such as Industry 4.0, artificial intelligence, machine learning, additive manufacturing, or blockchain;
- Tools and methodologies: this group of terms includes typical engineering tools and techniques, such as simulation, game theory, (robust) optimization, stochastic programming, system dynamics, case study, or multi-criteria decision making;
- Interrelated topics: terms in this category do not strictly refer to the area of supply chain disruptions, nor the more general area of risk or supply chain management. Rather, they introduce complementary topics, such as food security, food supply chain, small and medium enterprises, innovation, agility, or systematic review.
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Number of Papers | Period | Main Topic |
---|---|---|---|
[24] | 101 | 2006–2019 | Supply chain resilience in Small and Medium-sized Enterprises (SME) |
[25] | 46 | 2012–2022 | Supply chain resilience in SMEs in the context of the COVID-19 pandemic |
[26] | 517 | 2020–2022 | Trends in sustainability during and post the COVID-19 pandemic |
[27] | 151 | 2004–2021 | Coordination issues in the return supply chain |
[28] | 40 | 2002–2021 | Identification of key drivers for supply chain digitalization readiness |
[29] | 35 | 2020–2022 | Resilience strategies for disruption management in healthcare supply chains during the COVID-19 pandemic |
[30] | 191 | 2019–2021 | Effects of COVID-19 on the supply chain management |
[31] | 52 | 2017–2022 | Resilience practices in healthcare supply chain management, with a focus on purchasing challenges during the COVID-19 pandemic |
[32] | 68 | 2009–2020 | Artificial Intelligence and Big Data Analytics in Supply Chain Risk Management |
[33] | 50 | 2011–2020 | Ripple effect in supply chains |
[34] | 50 | 2020–2021 | Supply chains under disruptions due to COVID-19 pandemic, with a focus on the production and distribution of COVID-19 vaccine |
[35] | 135 | 2011–2021 | Practice and research gaps related to supply chains, and what characteristics should a supply chain have to be survivable |
[36] | 33 | 2011–2020 | Contribution of Industry 4.0 integration into supply chains to the enhancement of resilience |
[37] | 469 | 2020–2021 | Potential disruption-management strategies during the COVID-19 pandemic |
[38] | 87 | 2006–2021 | Impacts of additive manufacturing on the structure and dynamics of supply chains |
[39] | 173 | 2009–2021 | Main impacts of pandemics and epidemics on food supply chains and policies that can minimize these impacts |
[40] | 147 | 2019–2021 | How smart city solutions and technologies have contributed to enhancing resilience in cities during the COVID-19 pandemic |
[41] | 68 | 2019–2021 | COVID-19 impact on livestock systems and food security in developing countries |
[42] | 62 | 2020 | Delays and disruptions to cancer health care services due to COVID-19 pandemic |
[43] | 112 | 2020–2021 | How technology has tackled food supply chain challenges related to quality, safety, and sustainability |
[44] | 192 | 2017–2020 | Potential of blockchain for privacy and security challenges related to supply chain disruptions |
[45] | 32 | 2010–2020 | Impacts on the business environment of supply chains of previous epidemic outbreaks |
[46] | 455 | 2010–2019 | Supply chain risk management: review of the existing literature and exploration of risk factors |
[47] | 53 | 2000–2020 | Integration of lean and resilience paradigms |
[48] | 306 | n.d.–2020 | Inventory models with multiple sourcing options |
[49] | 2402 | 2008–2020 | Integration of sustainable supply chain management with organizational ambidexterity to manage disruptions effectively |
[50] | 77 | 2004–2018 | Review of the methods that are currently used for mitigating supply chain disruptions |
[51] | 1310 | 1999–2019 | Disruption risks in supply chain management |
[52] | 55 | 2004–2018 | Use of information technology in supply chain risk management |
[53] | 157 | 2000–2019 | How collaborations help supply chains respond and recover from a disruption |
[54] | 93 | 2008–2015 | Review of simulation methods that deal with risks in supply chain and types of data integration employed |
[55] | 27 | 2009–2020 | Psychological causes of panic buying |
[56] | 94 | 2017–2019 | Resilience analytics in supply chain management and modeling of the supply chain network dependence on other networks |
[57] | 77 | 2010–2019 | Use of machine learning algorithms for demand forecasting |
[58] | 1625 | 2009–2018 | Analysis of the most adopted theories in supply chain management, marketing and management |
[59] | 200 | n.d.–2017 | Multidisciplinary review about the concepts of agility and resilience |
[60] | 54 | 2000–2018 | Analysis of resilience focusing on upstream disruptions in agricultural value chains |
[61] | 27 | 2008–2018 | Use of blockchain in supply chain management context |
[62] | 41 | 1997–2017 | Cyber risk management in supply chain contexts |
[63] | 689 | 2010–2018 | Research themes on IoT and big data analytics in the field of supply chain management |
This study | 4239 | 2004–2023 | Supply chain disruptions |
2004–2008 | 2009–2013 | 2014–2018 | 2019–2023 | |
---|---|---|---|---|
Number of keywords | 251 | 847 | 1746 | 6687 |
Average frequency | 1.63 | 1.68 | 1.87 | 2.36 |
Frequency boundary | 2 | 2 | 2 | 3 |
Number of Periods | Number of Keywords | Percentage |
---|---|---|
1 | 7251 | 88.15% |
2 | 714 | 8.68% |
3 | 192 | 2.33% |
4 | 69 | 0.84% |
From/to | Final Classification (2019–2023) | ||||
---|---|---|---|---|---|
Emerging/Phantom | Intermittent | Trendy | Well-Established | ||
Initial classification (2004–2008) | emerging/phantom | 2 (supply chain planning; quantity discount) | 10 (supply risk management; buyback contract; supply management; dynamic programming; radio frequency identification; asymmetric information; coordination mechanism; safety stock; sourcing strategy; revenue sharing contract) | 0 | 13 (service level; transportation disruption; bullwhip effect; modelling; flexibility; analytic hierarchy process; inventory management; innovation; demand disruption; global supply chain; robustness; closed loop supply chain; stochastic programming) |
intermittent | 2 (contract; Petri net) | 6 (integration; terrorism; backup supplier; empirical research; contingency planning; business continuity planning) | 1 (sourcing) | 15 (supply uncertainty; resilience; agent-based model; visibility; coordination; information sharing; supply chain risk management; dual sourcing; supply chain vulnerabilities; agility; disaster; risk assessment; vulnerability; supply chain network; logistics) | |
trendy | 0 | 0 | 0 | 2 (inventory; supply chain design) | |
well-established | 1 (security) | 0 | 0 | 17 (supply chain; purchasing; supply chain disruption; supply chain management; disruption; risk management; supply chain coordination; supply disruption; supply chain risk; simulation; disruption management; uncertainty; risk; game theory; optimization; case study; supply risk) |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Solari, F.; Lysova, N.; Romagnoli, G.; Montanari, R.; Bottani, E. Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis. Sustainability 2024, 16, 7530. https://doi.org/10.3390/su16177530
Solari F, Lysova N, Romagnoli G, Montanari R, Bottani E. Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis. Sustainability. 2024; 16(17):7530. https://doi.org/10.3390/su16177530
Chicago/Turabian StyleSolari, Federico, Natalya Lysova, Giovanni Romagnoli, Roberto Montanari, and Eleonora Bottani. 2024. "Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis" Sustainability 16, no. 17: 7530. https://doi.org/10.3390/su16177530