Big Data Drives Efficiency: Amazon's Inventory Management with ML

  • Unique Paper ID: 163402
  • Volume: 10
  • Issue: 11
  • PageNo: 1621-1630
  • Abstract:
  • In the dynamic panorama of e-commerce, the synergy among information and generation defines achievement. Amazon, a worldwide leader inside the subject, exemplifies this fusion thru its adept integration of Big Data and Machine Learning (ML) into stock control. This study delves into the elaborate interaction among Big Data and ML inside Amazon's operations, unravelling the mechanisms at the back of its unheard of efficiency and patron pride. As digital shopping revolutionizes purchaser behaviour, Amazon's strategic deployment of Big Data analytics emerges as a guiding light. ML, however, stands because the actual catalyst, infusing Amazon's stock control with predictive prowess and flexibility. This paper navigates via Amazon's adventure from traditional stock control to the ML-pushed paradigm shift, elucidating how facts-driven choice-making shapes operational efficiency and consumer revel in. Through actual-international examples and case research, it showcases how ML-powered stock management no longer most effective optimizes operations however also sets new standards for purchaser pleasure. As we discover Amazon's ML algorithms and methodologies, this studies illuminates the transformative ability of records-pushed choice-making in the e-commerce realm. Ultimately, it serves as a guide through the intertwined geographical regions of Big Data and Machine Learning inside Amazon's inventory control, highlighting the pivotal function they play in driving innovation and performance within the virtual age.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{163402,
        author = {Aditya kumar and Yash pandey and Krishna Boob},
        title = {Big Data Drives Efficiency: Amazon's Inventory  Management with ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {1621-1630},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163402},
        abstract = {In the dynamic panorama of e-commerce, the synergy among information and generation defines achievement. Amazon, a worldwide leader inside the subject, exemplifies this fusion thru its adept integration of Big Data and Machine Learning (ML) into stock control. This study delves into the elaborate interaction among Big Data and ML inside Amazon's operations, unravelling the mechanisms at the back of its unheard of efficiency and patron pride. As digital shopping revolutionizes purchaser behaviour, Amazon's strategic deployment of Big Data analytics emerges as a guiding light. ML, however, stands because the actual catalyst, infusing Amazon's stock control with predictive prowess and flexibility. This paper navigates via Amazon's adventure from traditional stock control to the ML-pushed paradigm shift, elucidating how facts-driven choice-making shapes operational efficiency and consumer revel in. Through actual-international examples and case research, it showcases how ML-powered stock management no longer most effective optimizes operations however also sets new standards for purchaser pleasure. As we discover Amazon's ML algorithms and methodologies, this studies illuminates the transformative ability of records-pushed choice-making in the e-commerce realm. Ultimately, it serves as a guide through the intertwined geographical regions of Big Data and Machine Learning inside Amazon's inventory control, highlighting the pivotal function they play in driving innovation and performance within the virtual age.},
        keywords = {e-commerce, data management, operational efficiency, Amazon, inventory management, Big Data, Machine Learning, research paper, synergies, data-driven decision-making, operational excellence, transformative impact, inventory efficiency.},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 11
  • PageNo: 1621-1630

Big Data Drives Efficiency: Amazon's Inventory Management with ML

Related Articles