Optimizing manufacturing supply chains through intelligent data analytics: A case study of U.S. Industrial Operations

The rapidly evolving global market and growing complexity of industrial processes have made manufacturing supply chains a major managerial challenge. This research investigates how Intelligent Data Analytics (IDA) can optimize supply chain performance through a case study of U.S. industrial manufacturing operations. The study addresses persistent inefficiencies in traditional supply chains, such as poor forecasting, weak inventory control, and delayed decision-making. It aims to evaluate how predictive modeling and real-time analytics impact supply chain responsiveness, cost efficiency, and overall productivity. Employing a mixed-methods approach, the study combines quantitative historical supply chain data analysis with qualitative insights from industry professionals. Using computer algorithms and descriptive analytics, the research identified demand patterns that helped improve logistics and inventory management. In a mid-sized U.S. electronics manufacturing firm, implementing IDA led to a 25% increase in forecast accuracy, a 30% reduction in inventory levels, and a 20% decrease in lead times. The findings underscore the significant potential of analytics-based solutions to enhance operational efficiency and agility. As such, the study recommends that manufacturing organizations invest in scalable analytics tools and calls for supportive policies that encourage innovation and digital transformation in the sector. These measures are crucial to strengthening the global competitiveness of U.S. manufacturing.