Redefining Efficiency: Big Data Science in Supply Chain Design and Management

Authors

  • Devendra C. Yadav Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, India

DOI:

https://doi.org/10.37591/joprm.v13i1.7081

Keywords:

Supply chain optimization, Big data analytics, Data science, Machine learning, Optimization algorithms

Abstract

The combination of resources, tools, and applications in the field of supply chain management (SCM) is rapidly expanding, creating both opportunities and challenges. The term "big data" is commonly used to refer to the large and complex sets of data that are now available. These data are believed to have the potential to improve decision making and increase profitability. To effectively analyze and utilize these data, new methods of data science, such as predictive analytics, have been developed.

References

Barton, D., and Court, D. 2012. “Making Advanced Analytics Work for You.” Harvard Business Review 90: 79– 83.

Davenport, T., and Patil, D. 2012. “Data Scientist: The Sexiest Job of the 21st Century.” Harvard Business Review 90: 70– 76.

Mayer-Schönberger, V., and Cukier, K. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. New York: Houghton Mifflin Harcourt Publishing Company.

Provost, F., and Fawcett, T. 2013. “Data Science and Its Relationship to Big Data and Data-Driven Decision Making.” Big Data 1(1): 51– 59.

Dumbill, E., Liddy, E., Stanton, J., Mueller, K., and Farnham, S. 2013. “Educating the Next Generation of Data Scientists.” Big Data 1(1): 21– 27.

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Published

2023-07-11

Issue

Vol. 13 No. 1 (2023)

Section

Articles

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