Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Author(s):

  • Farzaneh Darbanian1 (Department for Logistics, University of Applied Sciences Upper Austria, Steyr, Austria & Josef Ressel-Centre for Predictive Value Network Intelligence, Steyr, Austria)
  • Patrick Brandtner1 (Department for Logistics, University of Applied Sciences Upper Austria, Steyr, Austria & Josef Ressel-Centre for Predictive Value Network Intelligence, Steyr, Austria)
  • Taha Falatouri1 (Department for Logistics, University of Applied Sciences Upper Austria, Steyr, Austria & Josef Ressel-Centre for Predictive Value Network Intelligence, Steyr, Austria & Tomas Bata University in Zlín, Faculty of Management and Economics, Czech Republic)
  • Mehran Nasseri1 (Department for Logistics, University of Applied Sciences Upper Austria, Steyr, Austria & Josef Ressel-Centre for Predictive Value Network Intelligence, Steyr, Austria)

Abstract:
In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. However, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

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