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

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
DOI 10.1007/s10479-016-2236-y
Open this paper in CoChat — free accountOpen PDF

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.

Claims the paper makes

0 of 22 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Restricting the search to scientific publications (articles and reviews) in peer-reviewed journals yielded 2564 relevant documents published during 2006-2016.

    “This search resulted in 2564 relevant documents, published during a 10-year period i.e. 2006-2016.”
    Quote in the paperSubstantial claim
  • Findingown data

    After excluding outliers from the co-citation network, the network has 233 nodes and 1096 edges.

    “On excluding these outliers we are left with a network having 233 nodes and 1096 edges.”
    Quote in the paperNot a substantial claim
  • Findingown data

    The top 20 journals appearing in the data published 286 articles in the field of 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.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Researchers in cluster 1 contributed theoretical and conceptual studies on Big Data.

    “It was found that researchers belonging to cluster 1 have contributed by giving theoretical and conceptual studies on Big Data.”
    Quote in the paperNot a substantial claim
  • Findingown data

    In the PageRank analysis, Boyd and Crawford (2012), the second highly cited paper, is in sixth position.

    “The second highly cited paper, Boyd and Crawford (2012), is in sixth position”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 22
  • The quote only partly states this claim. 1
  • May combine two claims. 9

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In both the title and keyword lists, the top keywords include a combination of Big Data, supply chain management, data mining and privacy.

    “For instance, in both tables the top keywords include a combination of Big Data, supply chain management, data mining and privacy.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    The study's initial Scopus search for 'Big Data' and 'Big Data AND Supply Chain' in title, abstract, and keywords yielded 7868 articles.

    “The initial search resulted in 7868 articles.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    Cluster 1 was targeted to study the concept of Big Data and analytics.

    “Hence, cluster 1 was targeted to study the concept of Big Data and analytics.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Wang is the top contributing author with 6 publications, followed by Li and Wang each with 5 publications.

    “It can be clearly observed that Wang with 6 publications dominates the list, and is followed by Li and Wang each with 5 publications.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    In the PageRank analysis, McAfee and Brynjolfsson (2012), which was third by citations, is second.

    “McAfee and Brynjolfsson ( 2012) which was at third position in Table 5 is at second position in Table 6.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Research in cluster 2 mainly revolved around the application of Big Data in supply chain management, the problems associated with data quality, and how this concept can be used to resolve supply chain problems.

    “Research in cluster 2 mainly revolved around the application of Big Data in SCM, the problems associated with data quality and how this concept can be used to resolve the problems of supply chains.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    There is uniformity in the use of keywords in paper titles and in the list of keywords.

    “By comparing these two tables it can be seen that there is a uniformity in the use of keywords in the title and the list of keywords.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the PageRank analysis, Jacobs (2009), not present in the citation top list, dominates the list.

    “Jacobs (2009), not present in Table 5, dominates the list in Table 6.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    McAfee and Brynjolfsson (2012) received 153 citations.

    “the article by McAfee and Brynjolfsson (2012), cited 153 times”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    After removing duplicates from the initial search results, 5486 papers remained.

    “On eliminating them, 5486 papers were left.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Studies in cluster 5 were concerned with developing methods and models for forecasting election results or social science problems, with a main focus on Bayes formula, and improving these forecasts by using specific predictors.

    “Studies in cluster 5 were concerned with developing methods and models that can be used in forecasting election results or social science problems with a main focus on Bayes formula, and in improving these forecasts by using specific predictors.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The number of publications on Big Data increased slowly from 2006 to 2013, but since then it has been increasing dramatically.

    “It can be clearly seen from the figure that the number of publications on Big Data increased slowly from 2006 to 2013, but since then it has been increasing dramatically.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.

  • Classified as probably not a substantial claim.

    Premisecitation

    Articles and reviews are categorised as certified knowledge.

    “Since Rodriguez and Navarro(2004) categorised articles and reviews as certified knowledge”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Rodriguez and Navarro(2004) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    In order to build applications for supply chain management and healthcare, clusters 3, 5, and 6 are needed to build robust optimised applied tools for Big Data and data mining.

    “Furthermore, in order to build applications for SCM and healthcare, clusters 3, 5, and 6 are needed to build robust optimised applied tools for Big Data and data mining.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Big Data and social media have not been thoroughly examined in the field of supply chain management.

    “Recently, Chae (2015) noted that Big Data and social media have not been thoroughly examined in the field of SCM.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chae (2015) source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Scopus is more comprehensive than Web-of-Science because WoS includes only ISI indexed journals, only 12,000 titles.

    “On comparing Scopus and Web-of-Science (WoS) databases, Yong-Hak (2013) observed that Scopus database is the more comprehensive since WoS includes only ISI indexed journals, only 12,000 titles.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Yong-Hak (2013)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    PageRank was introduced by Brin and Page (1998) to measure popularity and prestige and prioritise the results of web keyword searches.

    “To ensure that popularity and prestige are correlated, PageRank was introduced by Brin and Page (1998) to measure these concepts and prioritise the results of web keyword searches (Mishra et al. 2016a;2016b).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Brin and Page (1998) source

      Not checked in the free run.

    • cites Mishra et al. 2016a

      Not checked in the free run.

    • cites Mishra et al. 2016b

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    There is scope for future work in cluster 6.

    “while there is a scope of future work in cluster 6.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The most widely used method for measuring the importance of a paper is citation analysis.

    “The most widely used method for measuring the importance of a paper is citation analysis (Cronin and Ding 2011)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cronin and Ding 2011

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Conceptualising Big Data (Cluster 1) is the first step to building applications for Big Data analytics focusing on supply chain management (Cluster 2) and healthcare (Cluster 4).

    “In particular, conceptualising Big Data (Cluster 1) is the first step to building applications for Big Data analytics focusing on SCM (in Cluster 2) and healthcare (Cluster 4).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Co-citation analysis can be used in authors and/or publications order to track and study the relationship between authors, topics, journals or keywords.

    “Co-citation analysis can be used in authors and/or publications order to track and study the relationship between authors, topics, journals or keywords (Small 1973;Pilkington and Liston Heyes 1999).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Small 1973 source

      Not checked in the free run.

    • cites Pilkington and Liston Heyes 1999 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Integration of BDA in operations and supply chains aids firms in realizing their customers in a better way, minimizing cost to serve, managing risk efficiently, and in generating new and unexpected sources of revenue.

    “Understanding the role of BDA in improving SCM is extremely valuable since integration of BDA in operations and supply chains aids firms in realizing their customers in a better way, minimizing cost to serve, managing risk efficiently, and in generating new and unexpected sources of revenue (Sanders and Ganesan, 2015).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Sanders and Ganesan, 2015

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Data clustering aims at placing together sets of articles that share same characteristics.

    “Data clustering aims at placing together sets of articles that share same characteristics (Radicchi et al. 2004).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Radicchi et al. 2004 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The value of the damping factor d has been debated, with scholars suggesting a value of 0.85 and others a value of 0.5.

    “The value of parameter d has been the subject of debate, with scholars suggesting a value of 0.85 (Brin and Page 1998) while others a value of 0.5 (Chen et al. 2007).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Brin and Page 1998 source

      Not checked in the free run.

    • cites Chen et al. 2007 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Better conceptualisation and consensus of Big Data and use of particular tools and techniques will result in better applications of Big Data in SCM and healthcare.

    “it illustrates the relationships between the clusters and argues that better conceptualisation and consensus of Big Data and use of particular tools and techniques will result in better applications of Big Data in SCM (and healthcare)”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    This is the first study attempting to identify the top contributing authors, countries and key research topics related to Big Data and SCM.

    “To the best of our knowledge this is the first study attempting to identify the top contributing authors, countries and key research topics related to this field.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.

  • Classified as probably not a substantial claim.

    Interpretationnone given

    The current study contributes to the literature on Big Data and extends current reviews (Sagiroglu and Sinanc 2013; Fosso-Wamba et al. 2015; Gandomi and Haider 2015; Wang et al. 2016; Khorheh et al. 2015; Mishra et al. 2016a;2016b).

    “The current study contributes to the literature on Big Data and extends current reviews (Sagiroglu and Sinanc 2013;Fosso-Wamba et al. 2015;Gandomi and Haider 2015;Wang et al. 2016;Khorheh et al. 2015;Mishra et al. 2016a;2016b)”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Sagiroglu and Sinanc 2013

      Not checked in the free run.

    • cites Fosso-Wamba et al. 2015

      Not checked in the free run.

    • cites Gandomi and Haider 2015 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The review used bibliometric and network analysis based on Pilkington and Meredith (2009).

    “We used the bibliometric and network analysis for reviewing the literature based on Pilkington and Meredith (2009).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pilkington and Meredith (2009) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Other methods may result in other classifications of the literature.

    “We have used classified the literature in six research clusters.Other methods may result in other classifications.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The use of particular keywords may have yielded different results if other keywords were used.

    “We have used particular keywords for this research, and it may be that the use of other keywords may have yielded different results.”
    Quote in the paperNot a substantial claim
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.

  • Table 5 is described as both a keyword list and a title-word list

    Minor

    The text assigns the list-of-keywords results to Table 5, but Table 5 is labeled as words used in paper titles.

  • Citation rankings are attributed to Table 5

    Minor

    The PageRank comparison places cited papers in Table 5, although the paper identifies Table 5 as a list of words in paper titles, not a citation ranking.

  • Duplicate removal leaves fewer papers than the larger search result

    Minor

    The 6,534 papers returned by the first search alone must remain in the combined set after duplicates are removed. The reported total of 5,486 is smaller, so it cannot result from duplicate removal alone.

3 of 3

Cites

Flagged first, then by citations

8 of the 155 references couldn’t be matched to a known paper.

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

Sorted by citations

Related

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)
ORCID
Venue
Annals of Operations Research
Publisher
Springer Science and Business Media LLC
Published
2016-06-06
Volume
270
Issue
1-2
Pages
313-336
ISSN
  • 0254-5330 (ISSN-L)
  • 1572-9338
DOI
10.1007/s10479-016-2236-y
OpenAlex
W2416848540
Semantic Scholar
131cef9d6caf65a8cf7ad6b771c531c409e8930d
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
Licence
www.springer.com/tdm (tdm)
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 › Business, Management and Accounting › Management Information Systems › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review