Big Data Drives Efficiency: Amazon's Inventory Management with ML
Aditya kumar, Yash pandey, Krishna Boob
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.
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.
Article Details
Unique Paper ID: 163402

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 1621 - 1630
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