Big data analytics-enabled supply chain transformation: a literature review

Mondher Feki (), Imed Boughzala () and Samuel Fosso Wamba ()
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Mondher Feki: IMT-BS - DSI - Département Systèmes d'Information - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management
Imed Boughzala: IMT-BS - DSI - Département Systèmes d'Information - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management

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Abstract: Despite the rising potential of big data, a few studies have been conducted to examine it in the supply chain field. This article gives an overview of big data use in this field and underlines its potential role in the supply chain transformation by leading a systematic literature review. The results show that the big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive and these in turn influence supply chain processes and creates value. We conclude by highlighting future research directions.

Date: 2016-01-05
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Published in HICSS-49 : The Forty-Ninth Annual Hawaii International Conference on System Sciences, Jan 2016, Kauai, Hawaii, United States

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02332241

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