IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v270y2018i1d10.1007_s10479-016-2236-y.html
   My bibliography  Save this article

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

Author

Listed:
  • Deepa Mishra

    (IIT Kanpur)

  • Angappa Gunasekaran

    (University of Massachusetts Dartmouth)

  • Thanos Papadopoulos

    (University of Kent)

  • Stephen J. Childe

    (Plymouth University)

Abstract

As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on ‘Big Data and supply chain management (SCM)’, dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.

Suggested Citation

  • Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2236-y
    DOI: 10.1007/s10479-016-2236-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2236-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-016-2236-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tessier, Thomas H. & Armstrong, J. Scott, 2015. "Decomposition of time-series by level and change," Journal of Business Research, Elsevier, vol. 68(8), pages 1755-1758.
    2. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    3. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    4. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(10), pages 1671-1672, October.
    5. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(3), pages 519-520, March.
    6. Milé Terziovski, 2010. "Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view," Strategic Management Journal, Wiley Blackwell, vol. 31(8), pages 892-902, August.
    7. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    8. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    9. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    10. Goodwin, Paul, 2015. "When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts," Journal of Business Research, Elsevier, vol. 68(8), pages 1686-1691.
    11. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    12. Richard P. Larrick & Jack B. Soll, 2006. "Erratum--Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(2), pages 309-310, February.
    13. M.H. MacRoberts & B.R. MacRoberts, 2010. "Problems of citation analysis: A study of uncited and seldom-cited influences," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 1-12, January.
    14. Chaomei Chen & Fidelia Ibekwe-SanJuan & Jianhua Hou, 2010. "The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1386-1409, July.
    15. Michael H. MacRoberts & Barbara R. MacRoberts, 1989. "Problems of citation analysis: A critical review," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(5), pages 342-349, September.
    16. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
    17. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1371-1372, August.
    18. M.H. MacRoberts & B.R. MacRoberts, 2010. "Problems of citation analysis: A study of uncited and seldom‐cited influences," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 1-12, January.
    19. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    20. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    21. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(9), pages 1509-1510, September.
    22. Bent Flyvbjerg, 2014. "What You Should Know About Megaprojects, and Why: An Overview," Papers 1409.0003, arXiv.org.
    23. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(12), pages 1984-1985, December.
    24. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    25. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(11), pages 1835-1836, November.
    26. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    27. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(5), pages 852-853, May.
    28. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    29. Antonio‐Rafael Ramos‐Rodríguez & José Ruíz‐Navarro, 2004. "Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000," Strategic Management Journal, Wiley Blackwell, vol. 25(10), pages 981-1004, October.
    30. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    31. Ari Paloviita, 2009. "Stakeholder perceptions of alternative food entrepreneurs," World Review of Entrepreneurship, Management and Sustainable Development, Inderscience Enterprises Ltd, vol. 5(4), pages 395-406.
    32. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    33. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(7), pages 1193-1194, July.
    34. Mary J. Culnan, 1986. "The Intellectual Development of Management Information Systems, 1972--1982: A Co-Citation Analysis," Management Science, INFORMS, vol. 32(2), pages 156-172, February.
    35. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(4), pages 681-682, April.
    36. Peder Olesen Larsen & Markus Ins, 2010. "The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 575-603, September.
    37. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
    38. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
    39. Richard P. Larrick & Jack B. Soll, 2006. "Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(1), pages 111-127, January.
    40. Fahimnia, Behnam & Sarkis, Joseph & Davarzani, Hoda, 2015. "Green supply chain management: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 162(C), pages 101-114.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    2. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
    3. Eva Labro & Mark Lang & Jim Omartian, 2019. "Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data," Working Papers 19-02, Center for Economic Studies, U.S. Census Bureau.
    4. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    5. Lidong Wang & Cheryl Ann Alexander, 2015. "Big Data Driven Supply Chain Management and Business Administration," American Journal of Economics and Business Administration, Science Publications, vol. 7(2), pages 60-67, June.
    6. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    7. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    8. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    9. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    10. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    11. Verma, Sanjeev & Yadav, Neha, 2021. "Past, Present, and Future of Electronic Word of Mouth (EWOM)," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 111-128.
    12. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    13. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    14. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    15. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    16. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    17. Shuihua Han & Yufang Fu & Bin Cao & Zongwei Luo, 2018. "Pricing and bargaining strategy of e-retail under hybrid operational patterns," Annals of Operations Research, Springer, vol. 270(1), pages 179-200, November.
    18. Geoffrey M. Hodgson & Juha-Antti Lamberg, 2018. "The past and future of evolutionary economics: some reflections based on new bibliometric evidence," Evolutionary and Institutional Economics Review, Springer, vol. 15(1), pages 167-187, June.
    19. Morgan Swink & Kejia Hu & Xiande Zhao, 2022. "Analytics applications, limitations, and opportunities in restaurant supply chains," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3710-3726, October.
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2236-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.