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Investigating the Enablers of Big Data Analytics on Sustainable Supply Chain

Author

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  • Lineth Rodríguez

    (LS2N - Laboratoire des Sciences du Numérique de Nantes - UN UFR ST - Université de Nantes - UFR des Sciences et des Techniques - UN - Université de Nantes - ECN - École Centrale de Nantes - CNRS - Centre National de la Recherche Scientifique - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris])

  • Mihalis Giannakis

    (Audencia Business School)

  • Catherine da Cunha

    (LS2N - Laboratoire des Sciences du Numérique de Nantes - UN UFR ST - Université de Nantes - UFR des Sciences et des Techniques - UN - Université de Nantes - ECN - École Centrale de Nantes - CNRS - Centre National de la Recherche Scientifique - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris], ECN - École Centrale de Nantes)

Abstract

Scholars and practitioners already shown that Big Data and Predictive Analytics (BDPA) can play a pivotal role in transforming and improving the functions of sustainable supply chain analytics (SSCA). However, there is limited knowledge about how BDPA can be best leveraged to grow social, environmental and financial performance simultaneously. Therefore, with the knowledge coming from literature around SSCA, it seems that companies still struggle to implement SSCA practices. Researchers agree that is still a need to understand the techniques, tools, and enablers of the basics SSCA for its adoption; this is even more important to integrate BDPA as a strategic asset across business activities. Hence, this study will investigate, for instance, what are the enablers of SSCA, and what are the tools and techniques of BDPA that enable 3BL of sustainability performance through SCA. For this purpose, we will collect responses from structured remote questionnaires by targeting highly experienced supply chain professionals. Later, we are going to analyze the data using a well-known statistical analysis such as exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and logistics regression.

Suggested Citation

  • Lineth Rodríguez & Mihalis Giannakis & Catherine da Cunha, 2018. "Investigating the Enablers of Big Data Analytics on Sustainable Supply Chain," Post-Print hal-01982533, HAL.
    • Handle: RePEc:hal:journl:hal-01982533
    Note: View the original document on HAL open archive server: https://hal.science/hal-01982533
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    References listed on IDEAS

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    Keywords

    sustainability; supply chain analytics; big data and predictive analytics; enablers Research Proposal;
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