Proposals for Addressing Research Gaps at the Intersection of Data Analytics and Supply Chain Management

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7 Citations (Scopus)


Data Analytics (DA) plays an important role in improving and optimizing the processes in a Supply Chain (SC) network. Due to a huge amount of data generated in the various SC processes, the role of DA in Supply Chain Management (SCM) is becoming increasingly evident. Organizations have already invested heavily in applying various DA technologies to their SC networks. Several reviews have been conducted in different domains of SCM indicating applications and limitations of DA in SCM. As the research domain of DA applications in SCM matures, it is necessary to identify and address the research gaps that exist at the intersection of these domains. The paper qualitatively examines recent review papers in the domain of DA in SCM to identify and outline prominent ways that DA is currently applied in SCM, what potential future opportunities stated and what challenges affecting DA application in SCM are existing. Prominent use cases of DA in SCM include i) forecasting demand, ii) product development, iii) logistics route planning and iv) lean SC development. However, there is no prominent, unique future application list of DA in SCM since the findings vary across the papers. Prominent challenges affecting DA in SCM include i) lack of collaboration, ii) data sharing problems, iii) risks associated with BD management and iv) lack of skilled experts. Lastly, this article provides two conceptual ideas for addressing these prominent DA challenges in SCM: first, a framework for data analytics enabling collaboration in SCM by using transparent data questions and second, a blockchain-based data management approach in SC networks.

Original languageEnglish
Pages (from-to)338-346
Number of pages9
JournalJournal of Advances in Information Technology
Issue number4
Publication statusPublished - Aug 2022


  • data analytics
  • data analytics research gaps
  • SCM challenges
  • SCM research gaps
  • supply chain management


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