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Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review

Author

Listed:
  • Elena Barzizza

    (Department of Management Engineering, University of Padova, 35100 Padova, Italy)

  • Nicolò Biasetton

    (Department of Management Engineering, University of Padova, 35100 Padova, Italy)

  • Riccardo Ceccato

    (Department of Management Engineering, University of Padova, 35100 Padova, Italy)

  • Luigi Salmaso

    (Department of Management Engineering, University of Padova, 35100 Padova, Italy)

Abstract

Owing to the development of the technologies of Industry 4.0, recent years have witnessed the emergence of a new concept of supply chain management, namely Supply Chain 4.0 (SC 4.0). Huge investments in information technology have enabled manufacturers to trace the intangible flow of information, but instruments are required to take advantage of the available data sources: big data analytics (BDA) and machine learning (ML) represent important tools for this task. Use of advanced technologies can improve supply chain performances and support reaching strategic goals, but their implementation is challenging in supply chain management. The aim of this study was to understand the main benefits, challenges, and areas of application of BDA and ML in SC 4.0 as well as to understand the BDA and ML techniques most commonly used in the field, with a particular focus on nonparametric techniques. To this end, we carried out a literature review. From our analysis, we identified three main gaps, namely, the need for appropriate analytical tools to manage challenging data configurations; the need for a more reliable link with practice; the need for instruments to select the most suitable BDA or ML techniques. As a solution, we suggest and comment on two viable solutions: nonparametric statistics, and sentiment analysis and clustering.

Suggested Citation

  • Elena Barzizza & Nicolò Biasetton & Riccardo Ceccato & Luigi Salmaso, 2023. "Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review," Stats, MDPI, vol. 6(2), pages 1-21, May.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:2:p:38-616:d:1139579
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    References listed on IDEAS

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    1. 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.
    2. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    3. Jebum Pyun & Jin Sung Rha, 2021. "Review of Research on Digital Supply Chain Management Using Network Text Analysis," Sustainability, MDPI, vol. 13(17), pages 1-24, September.
    4. Raj Kumar Bachar & Shaktipada Bhuniya & Santanu Kumar Ghosh & Biswajit Sarkar, 2022. "Controllable Energy Consumption in a Sustainable Smart Manufacturing Model Considering Superior Service, Flexible Demand, and Partial Outsourcing," Mathematics, MDPI, vol. 10(23), pages 1-29, November.
    5. Singh, Akshit & Shukla, Nagesh & Mishra, Nishikant, 2018. "Social media data analytics to improve supply chain management in food industries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 398-415.
    6. Tino T. Herden & Steffen Bunzel, 2018. "Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study," Logistics, MDPI, vol. 2(2), pages 1-20, May.
    7. Yu, Wantao & Chavez, Roberto & Jacobs, Mark A. & Feng, Mengying, 2018. "Data-driven supply chain capabilities and performance: A resource-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 371-385.
    8. Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.
    9. Fortunato Pesarin & Luigi Salmaso, 2010. "Finite-sample consistency of combination-based permutation tests with application to repeated measures designs," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(5), pages 669-684.
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