IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v59y2021i6p1875-1900.html
   My bibliography  Save this article

Role of Big Data Analytics in supply chain management: current trends and future perspectives

Author

Listed:
  • Sumit Maheshwari
  • Prerna Gautam
  • Chandra K. Jaggi

Abstract

It is a widely accepted fact that almost every research or business revolves around Data. Data from various business sectors has been growing sharply and the management of this massive amount of data is the biggest professional crunch these days. The notion of Big Data Analytics (BDA) is a prominent facet that delivers the best possible solution to decision-makers for efficiently handling the problems related to huge data. The key role of BDA in the area of Supply Chain Management (SCM), Logistics Management (LM), and Inventory Management (IM) is of utmost significance as it optimises the business operations by analyzing customer behaviour. Motivated with the promising paybacks of the BDA, a recent review from the year 2015–2019 is presented in this paper. Further, the significance of BDA in SCM, LM, and IM has been highlighted by studying 58 papers, which have been sorted after a detailed study of 260 papers, collected through the Web of Science (WoS) database. The findings and observations give state-of-the-art insights to scientists and business professionals by presenting an exhaustive list of the progress made and challenges left untackled in the field of BDA in SCM, LM, and IM.

Suggested Citation

  • Sumit Maheshwari & Prerna Gautam & Chandra K. Jaggi, 2021. "Role of Big Data Analytics in supply chain management: current trends and future perspectives," International Journal of Production Research, Taylor & Francis Journals, vol. 59(6), pages 1875-1900, March.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:6:p:1875-1900
    DOI: 10.1080/00207543.2020.1793011
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2020.1793011
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2020.1793011?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zahoor, Nadia & Khan, Zaheer & Shenkar, Oded, 2023. "International vertical alliances within the international business field: A systematic literature review and future research agenda," Journal of World Business, Elsevier, vol. 58(1).
    2. Zhang, Qingyu & Gao, Bohong & Luqman, Adeel, 2022. "Linking green supply chain management practices with competitiveness during covid 19: The role of big data analytics," Technology in Society, Elsevier, vol. 70(C).
    3. Ehsan Najafnejhad & Mahdieh Tavassoli Roodsari & Somayeh Sepahrom & Mojtaba Jenabzadeh, 2021. "A mathematical inventory model for a single-vendor multi-retailer supply chain based on the Vendor Management Inventory Policy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 579-586, June.
    4. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
    5. Ayman wael AL-Khatib & Ahmed Shuhaiber, 2022. "Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    6. Tan, Weng Chun & Sidhu, Manjit Singh, 2022. "Review of RFID and IoT integration in supply chain management," Operations Research Perspectives, Elsevier, vol. 9(C).
    7. Schoenherr, Tobias, 2023. "Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    8. Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    9. Utama, Dana Marsetiya & Santoso, Imam & Hendrawan, Yusuf & Dania, Wike Agustin Prima, 2022. "Integrated procurement-production inventory model in supply chain: A systematic review," Operations Research Perspectives, Elsevier, vol. 9(C).
    10. Kamlesh Kumar Pandey & Diwakar Shukla, 2022. "Stratified linear systematic sampling based clustering approach for detection of financial risk group by mining of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1239-1253, June.
    11. Manju Saroha & Dixit Garg & Sunil Luthra, 2022. "Analyzing the circular supply chain management performance measurement framework: the modified balanced scorecard technique," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 951-960, June.
    12. Kazancoglu, Yigit & Sagnak, Muhittin & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Pala, Melisa Ozbiltekin, 2021. "A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    13. Fernando ALMEIDA & Samantha LOW-CHOY, 2021. "Exploring The Relationship Between Big Data And Firm Performance," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(3), pages 43-57, September.
    14. Zbysław Dobrowolski, 2021. "Internet of Things and Other E-Solutions in Supply Chain Management May Generate Threats in the Energy Sector—The Quest for Preventive Measures," Energies, MDPI, vol. 14(17), pages 1-11, August.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:59:y:2021:i:6:p:1875-1900. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.