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The Impact of Data Analytics on High Efficiency Supply Chain Management

Author

Listed:
  • Sakila Akter JAHAN

    (Independent University, Bangladesh, Dhaka, Bangladesh)

  • Mesbaul Haque SAZU

    (Case Western Reserve University, Cleveland, USA)

Abstract

Change is inevitable, so when supply chain (SC) managers strategize for future years, they must deal with challenges of the global supply chain management (SCM) issues. Leading trends over the last couple of years tend to be the increasing value of big data and analyzing the information through analytics. The information has tremendous value; businesses must capitalize on the assortment of information by proper and in-depth evaluation with the usage of big data analytics (BDA). This article seeks to spotlight the changing dynamics of the SC managing atmosphere, to recognize the way the two leading trends will influence SCM in future, to demonstrate the advantages which may be derived, and to generate suggestions for providing SC managers if BDA is adopted. The process of deriving value from the large quantities of information within the SCM is defined. It is demonstrated, through examples, the way SCM location might be influenced by these brand-new developments and trends. Within the examples, BDA have been adopted, utilized and applied effectively. Big data and analytics to draw out value coming from the information can create a big influence. It is clearly suggested that chain administrators pay attention to these two trends, since better usage of BDA can ensure they keep abreast with innovations modifications, which could help improve company competitiveness.

Suggested Citation

  • Sakila Akter JAHAN & Mesbaul Haque SAZU, 2022. "The Impact of Data Analytics on High Efficiency Supply Chain Management," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(7), pages 62-72, July.
    • Handle: RePEc:ahd:journl:v:3:y:2022:i:7:p:62-72
    DOI: 10.37945/cbr.2022.07.07
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    References listed on IDEAS

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    1. Bresciani, Stefano & Ciampi, Francesco & Meli, Francesco & Ferraris, Alberto, 2021. "Using big data for co-innovation processes: Mapping the field of data-driven innovation, proposing theoretical developments and providing a research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    2. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
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    More about this item

    Keywords

    supply chain management; big data analytics;

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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