Big Data-Driven Insights in Modern Supply Chain Analytics: Opportunities, Challenges, and Future Directions
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P105Keywords:
Big Data, Supply Chain Analytics, Demand Forecasting, Inventory Optimization, Artificial Intelligence, Blockchain, Edge Computing, Cloud Computing, SustainabilityAbstract
Big data technologies have revolutionized supply chain analytics, and now organizations can convert overwhelming, complex datasets into actionable insights. This paper examines the multi-faceted nature of big data in contemporary supply chains, paying particular attention to its opportunity to optimize decision-making, operational efficiency, and strategic planning. Central opportunities, including demand forecasting, inventory optimization, supplier risk management, real-time visibility, and sustainability initiatives, are reviewed to highlight big data analytics’ real benefits. Even though these advances have been made, there are still major challenges, such as issues of data quality, concerns of privacy and security, interoperability at the level of systems, and a shortage of skills, in addition to the necessity of scalable real-time solutions for processing. The paper further looks at developing technologies such as artificial intelligence, block chain, edge computing, and cloud platforms that transform supply chain analytics and enable more intelligent, transparent, and efficient operations. Future trends include autonomous systems, collaboration across the ecosystem, sustainability integration, and the future impact of quantum computing on supply chain optimization. By integrating the existing trends and concerns, this research presents an encompassing picture of how big data-based insights revolutionize supply chain management. It suggests directions for research and action in an ever-data-focused world
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