Cited by 382Big Data and Business IntelligenceImpact of AI and Big Data on Business and SocietySupply Chain and Inventory Management

The DOI matches its Crossref record, with a small difference in title or year; 147 of the 155 references it cites were found in a registry or an index; none of the 22 sources its claims cite is checked for support yet.

Big Data and supply chain management: a review and bibliometric analysis

Open access
Mishra, Deepa, Gunasekaran, Angappa, Papadopoulos, Thanos, Childe, Stephen J.·2018·Annals of Operations Research

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This paper reviews and maps research on Big Data and supply chain management (SCM) from 2006 to 2016 using bibliometric and network analyses of 286 articles. It identifies influential authors, countries, works, and six research clusters, and proposes future directions for Big Data in SCM.

Key findings

  • The review evaluated 286 articles published over 10 years (2006–2016), drawn from an initial 7,868 Scopus records and refined to 2,564 relevant documents before focusing on the top 20 journals.
  • Wang was the top contributing author with 6 publications, followed by Li and Wang with 5 each; the United States, China, and United Kingdom were the major contributing countries.
  • The most cited work was Chiu et al. (2006) with 804 citations, followed by Boyd and Crawford (2012) with 351 citations and McAfee and Brynjolfsson (2012) with 153 citations; PageRank analysis placed Jacobs (2009) at the top, with McAfee and Brynjolfsson (2012) second.
  • Co-citation analysis of a 233-node, 1,096-edge network produced six clusters: conceptualisation of Big Data and analytics; Big Data and SCM; Big Data tools and algorithms; Big Data applications in healthcare; Big Data and forecasting; and data mining and applications, with a modularity index of 0.19.

Methods

  • The authors used a five-step literature review process, searching Scopus for “Big Data” and “Big Data AND Supply Chain” in title, abstract, and keywords.
  • Initial results were 7,868 articles; duplicates were removed to leave 5,486, then the search was restricted to peer-reviewed journal articles and reviews, yielding 2,564 documents from 2006–2016.
  • Bibliometric analysis was performed with BibExcel, and network analysis with Gephi, including citation analysis, PageRank, co-citation analysis, and Louvain modularity-based clustering.

Limitations

  • The paper states that using particular keywords may have yielded different results with other keywords.
  • The paper states that it used bibliometric and network analysis based on Pilkington and Meredith (2009), and other methods may be used.
  • The paper states that the six-cluster classification may differ under other methods; evident from the text, the review relies on Scopus only and on the top 20 journals for the 286-article analysis, which may limit coverage.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s10479-016-2236-y

Cites

147 of 147 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

29 Own result · 16 Not checked yet · 8 Own interpretation · 3 Own method · 3 No basis given

Verify all 22 references — free account

Show all 29 claims

  • Probably not a substantial claim. 22
  • The quote only partly states this claim. 1
  • It may state two separate claims as one. 9
30 of 30
  • The paper relies on this without support.

    The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.

    “we identified the top 20 journals appearing in the data, and it was found that these journals have published 286 articles in this field of research.”
  • The paper relies on this without support.

    The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.

    “Our review was based on the review of the literature on Big Data and SCM using 286 articles published in past 10 years.”
    “We have used particular keywords for this research, and it may be that the use of other keywords may have yielded different results.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

3 of 3

Citations

Cites

Show all 155 references

Research works printed but not linked: 10

  1. Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76.
  2. Diebold, F. X. (2012). A personal perspective on the origin(s) and development of “Big Data”: The phenomenon, the term, and the discipline. Scholarly Paper No. ID 2202843, Social Science Research Network. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstractid=2202843 .
  3. Elberse, A. (2008). Should you invest in the long tail? HBS Centennial Issue Harvard Business Review, 86(7–8), 88–96. (HBS Centennial Issue).
  4. Hopkins, M. S., Lavalle, S., & Balboni, F. (2010). The new intelligent enterprise: 10 insights: A first look at the new intelligent enterprise survey. MIT Sloan Management Review, 52(1), 22.
  5. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data: Analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.
  6. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big Data: The Next Frontier for Innovation. McKinsey Global Institute: Competition and Productivity.
  7. Mishra, D., Luo, Z., Jiang, S., Papadopoulos, T., & Dubey, R. (2016b). A bibliographic study on Big Data: Concepts. Trends and Challenges (Accepted Paper): Business Process Management Journal.
  8. Oxford Economics. (2011). The new digital economy: How it will transform business. http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CD0QFjAC&url=http%3A%2F%2Fwww.businessandleadership.com%2Fdownload%2Ffs%2Fdoc%2Freports%2Fthe-new-digital-economy-1.pdf&ei=DeHUUpuWM66QiQfjs4HIBA&usg=AFQjCNGQKXaQccqoEy8Na0ctStXgaJPH1Q&bvm=bv.59378465,d.aGc . Accessed 10 January 14.
  9. Records, R. L., & Fisher, Q. K. (2014). Manufacturers connect the dots with Big Data and analytics. Computer Science Corporation (pp. 1–6). Accessed 31 May 2014.
  10. Wong, D. (2012). Data is the next frontier, analytics the new tool: Five trends in big data and analytics, and their implications for innovation and organisations. London: Big Innovation Centre.

Cited by

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Items 301–380 of 380

Metadata

Authors
Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J.
Affiliations
  • Indian Institute of Technology Kanpur (IN)
  • University of Massachusetts Dartmouth (US)
  • University of Plymouth (GB)
Venue
Annals of Operations Research
Published
2016-06-06
Volume
270
Issue
1-2
Pages
313-336
ISSN
  • 0254-5330 (ISSN-L)
  • 1572-9338
OpenAlex
W2416848540
Cited by
382
References
155
Citations by year
2026: 18 · 2025: 37 · 2024: 47 · 2023: 74 · 2022: 49 · 2021: 51 · 2020: 59 · 2019: 19 · 2018: 17 · 2017: 9 · 2016: 2
FWCI
28.12
Type
journal article
Language
en
Open access
Locations
  • Annals of Operations Research (publishedVersion)
  • Kent Academic Repository (University of Kent) (submittedVersion)
  • PEARL (University of Plymouth) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Supply Chain and Inventory Management
Keywords
big data, supply chain management, big data analytics, research trends, research clusters, future research directions, citation analysis, PageRank, research collaboration

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