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Big-Data and Service Supply chain management: Challenges and opportunities
[Big-Data et Service Supply chain management: Challenges et opportunités]

Author

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  • Badr Bentalha

    (USMBA - Université Sidi Mohamed Ben Abdellah)

Abstract

The Big-Data describes the large volume of data used by economic actors. The data is analysed quickly to formulate instant analysis and data storage. This system is useful for several economic fields such as logistics and supply chain management (SCM). The latter is a management of physical and information flows, from customer to customer and from supplier to supplier, in order to offer a satisfactory response to customer needs. SCM was born and flourished in an industrial context. Nevertheless, several cur of Big-Data help improve the performance of supply chain management in service companies? To answer this question, we will define the concepts of SCM in services, focusing on the concept of Big-Data while analyzing the impact of Big-Data on the efficiency of SCM in service companies.

Suggested Citation

  • Badr Bentalha, 2020. "Big-Data and Service Supply chain management: Challenges and opportunities [Big-Data et Service Supply chain management: Challenges et opportunités]," Post-Print hal-02680861, HAL.
  • Handle: RePEc:hal:journl:hal-02680861
    DOI: 10.5281/zenodo.3607357
    Note: View the original document on HAL open archive server: https://hal.science/hal-02680861
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Tuncdan Baltacioglu & Erhan Ada & Melike D. Kaplan & Oznur Yurt And & Y. Cem Kaplan, 2007. "A New Framework for Service Supply Chains," The Service Industries Journal, Taylor & Francis Journals, vol. 27(2), pages 105-124, March.
    3. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    4. Tsan‐Ming Choi & James H. Lambert, 2017. "Advances in Risk Analysis with Big Data," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1435-1442, August.
    5. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
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    More about this item

    Keywords

    SCM; Service Logistics; Service Supply Chain; Big-Data; Supply chain; Logistique de services; Digital Supply Chain; Entreprises de Services;
    All these keywords.

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