Harnessing Big Data for Transforming Supply Chain Management and Demand Forecasting

Author(s)	Adya Mishra
Country	United States
Abstract	Evolution of big data and predictive analytics has initiated a paradigm shift in modern supply chain management. Traditional supply chain design and demand forecasting methods that relied on historical, often static data no longer suffice in an environment characterized by rapid market fluctuations, evolving consumer behaviors, and global complexities. Predictive analytics—powered by large and diverse data sets—enables supply chain stakeholders to effectively anticipate demand changes, optimize resource allocation, and mitigate risks. This review paper provides an in-depth examination of how big data-driven predictive analytics is transforming supply chain design and demand forecasting. We discuss the foundational concepts of big data, explore cutting-edge analytical approaches, analyze the impact on strategic and operational decisions, and identify challenges and prospects. By consolidating key technical insights and best practices, this paper aims to serve as a comprehensive resource for supply chain professionals, data scientists, and researchers exploring how to leverage data-driven decision-making to create resilient, agile, and transparent supply chains.
Keywords	Data Science, Big Data, Supply Chain, Data-Driven Decision Making
Field	Engineering
Published In	Volume 3, Issue 6, November-December 2021
Published On	2021-11-02
DOI	https://doi.org/10.36948/ijfmr.2021.v03i06.36897
Short DOI	https://doi.org/g84d8k

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International Journal For Multidisciplinary Research