Integrating Business Intelligence and Data Analytics to Optimize End-to-End Supply Chain Performance in U.S. Manufacturing and Logistics Networks

Abstract

The growing complexity of the manufacturing and logistics networks in the United States has increased the necessity of using data-driven decision-making across the supply chain. Business Intelligence (BI) and Data Analytics (DA) have become essential facilitators toward enhancing visibility, coordination, and performance in end-to-end supply chain activities. This paper looks at the way that the conglomeration of BI and DA tools can streamline the performance in the supply chain of manufacturing and logistics networks in the United States. The article utilizes a conceptual and analytical framework based on supply chain management and information systems theory in exploring how descriptive, predictive, and prescriptive analytics can be used to improve demand forecasting, inventory management, transportation planning, and operational resilience. According to the findings, organizations that use integrated capabilities of BI and DA gain access to better real-time visibility, lower operational costs, increased responsiveness to disruptions, and increased informed strategic decisions. The paper also identifies the major implementation barriers, such as data silos, systems interoperability, and organizational preparedness. Combining existing practices and performance results, the paper will add research and practical significance to the scholarly literature on how data-driven intelligence can change end-to-end supply chain performance in the U.S. manufacturing and logistics ecosystem.