Transforming Supply Chain Analytics with Real-Time Data and Cloud Data Warehousing: A Strategic Framework
Year: 2025
Type: article
Abstract: This paper explores the transformative role of real-time data and cloud data warehousing in optimizing supply chain analytics. As supply chains grow increasingly complex, businesses are increasingly turning to data-driven strategies to enhance operational efficiency, reduce costs, and improve responsiveness to market demands. Real-time data provides the foundation for proactive decision-making, enabling organizations to monitor supply chain activities continuously and make informed adjustments to minimize disruptions. The integration of cloud data warehousing offers scalable, flexible, and cost-efficient solutions for managing vast quantities of supply chain data, allowing for seamless data centralization, integration, and analytics. This strategic framework, combining real-time data with cloud platforms, not only improves operational agility but also enhances the ability to predict demand, optimize inventory, and mitigate risks. Despite the substantial benefits, challenges related to data integration, organizational resistance, and security concerns persist. This paper outlines these challenges and proposes strategies for overcoming them. Furthermore, it highlights future research directions, including the application of artificial intelligence, the role of IoT in data collection, and the ethical and regulatory considerations of cloud adoption. Ultimately, the integration of these technologies is poised to reshape the future of supply chain analytics, fostering greater efficiency and competitiveness in an increasingly dynamic global marketplace.
Authors: Brainforge, Texas, USA, Oluwademilade Aderemi Agboola, Abel Chukwuemeke Uzoka, Olanrewaju Oluwaseun Ajayi, Bright Chibunna Ubanadu, Signal Alliance Technology Holding, Nigeria, Andrew Ifesinachi Daraojimba, Signal Alliance Technology Holding, Nigeria, Chisom Elizabeth Alozie
Institutions: Goldman Sachs (United States), University of the Cumberlands
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4411030446