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

Elena Barzizza, Nicolò Biasetton, Riccardo Ceccato and Luigi Salmaso ()
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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

Stats, 2023, vol. 6, issue 2, 1-21

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.

Keywords: Supply Chain 4.0; machine learning; big data analytics; advantages; disadvantages; area of application; nonparametric statistics; sentiment analysis (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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