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Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model

Author

Listed:
  • Samia Gamoura

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

  • Ridha Derrouiche

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

  • David Damand

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

  • Marc Barth

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

Abstract

When supply chain management (SCM) intersects with Big Data Analytics (BDA), uncountable opportunities for research emerge. Unfortunately, how analytics can be applied to supply chain processes is still unclear for both academics and industries. To better connect SC processes needs and what BDA offer, we present a structured review of academic literature that addresses BDA methods in SCM using the supply chain operations reference (SCOR) model. The literature since 2001 is reviewed to provide a taxonomy framework resulting in a nomenclature grids and a SCOR-BDA matrix. The most important result of this paper indicates a clear disparity and points to an urgent need to bring the efforts closer in a collaborative way for more intelligent use of BDA in SCM. Furthermore, this paper highlights a misalignment between data scientists and SC managers in BDA applicability. It also highpoints upcoming research tracks and the main gaps that need to be stunned.

Suggested Citation

  • Samia Gamoura & Ridha Derrouiche & David Damand & Marc Barth, 2019. "Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model," Post-Print hal-04578512, HAL.
    • Handle: RePEc:hal:journl:hal-04578512
    DOI: 10.1080/09537287.2019.1639839
    as

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