Data Analytics for Supply Chain Management: Leveraging Data to Drive Decision-Making and Improve Efficiency
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Abstract
Over the recent years, integration of data analytics in supply chain management (SCM) has been a force for change, enabling companies to enhance decision-making, optimize operations, and enhance overall efficiency. This paper outlines the role of data analytics in driving supply chain optimization and its impact on forecasting, inventory management, risk management, and sustainability. Integration of intelligent technologies like artificial intelligence (AI), machine learning, blockchain, and real-time data analytics has transformed SCM to a great extent, enabling companies to forecast demand fluctuations, reduce operational costs, and ensure transparency throughout the supply chain. However, in the midst of such technological advancements, there are still areas of setback in the global adoption of such technologies with concerns in data quality, integration, and the availability of skilled resources. While research assures the potential delivered by big data and predictive analytics, much of the literature focuses on single-technology implementations, so there is little knowledge on the implementation of such technologies by companies for end-to-end supply chain optimization. Furthermore, real-time decision-making and collaborative analytics among enterprises still remain less studied. Thus, this study attempts to bridge these research gaps by performing a detailed review of contemporary data analytics usage in SCM, identifying what restricts such adoption, and providing recommendations on how to overcome such challenges. Overcoming such challenges, firms are able to use data analytics more effectively to develop agile, nimble, and sustainable supply chains that can accommodate changing market conditions
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