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Big Data In Supply Chain Management: An Exploratory Study

Author

Listed:
  • Gheorghe MILITARU

    (Politehnica University of Bucharest, Romania)

  • Massimo POLLIFRONI

    (University of Turin, Italy)

  • Alexandra IOANID

    (Politehnica University of Bucharest, Romania)

Abstract

The objective of this paper is to set a framework for examining the conditions under which the big data can create long-term profitability through developing dynamic operations and digital supply networks in supply chain. We investigate the extent to which big data analytics has the power to change the competitive landscape of industries that could offer operational, strategic and competitive advantages. This paper is based upon a qualitative study of the convergence of predictive analytics and big data in the field of supply chain management. Our findings indicate a need for manufacturers to introduce analytics tools, real-time data, and more flexible production techniques to improve their productivity in line with the new business model. By gathering and analysing vast volumes of data, analytics tools help companies to resource allocation and capital spends more effectively based on risk assessment. Finally, implications and directions for future research are discussed.

Suggested Citation

  • Gheorghe MILITARU & Massimo POLLIFRONI & Alexandra IOANID, 2015. "Big Data In Supply Chain Management: An Exploratory Study," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 6, pages 103-108, December.
    • Handle: RePEc:cmj:networ:y:2015:i:5:p:103-108
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    References listed on IDEAS

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    2. Fernando Bernstein & Awi Federgruen, 2007. "Coordination Mechanisms for Supply Chains Under Price and Service Competition," Manufacturing & Service Operations Management, INFORMS, vol. 9(3), pages 242-262, January.
    3. Da Silveira, Giovani & Borenstein, Denis & Fogliatto, Flavio S., 2001. "Mass customization: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 72(1), pages 1-13, June.
    4. Doug Howe & Maria Costanzo & Petra Fey & Takashi Gojobori & Linda Hannick & Winston Hide & David P. Hill & Renate Kania & Mary Schaeffer & Susan St Pierre & Simon Twigger & Owen White & Seung Yon Rhee, 2008. "The future of biocuration," Nature, Nature, vol. 455(7209), pages 47-50, September.
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    More about this item

    Keywords

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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General

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