A Data-Driven Framework for Optimizing Smart Infrastructure Projects Through Integrated Business Analytics, Supply Chain Resilience, and Engineering Management in the U.S. Manufacturing Sector

Abstract

Development of intelligent infrastructure within American manufacturing industry has opened up major potentials to creating more efficiency, flexibility and sustainability. Yet, the existing approaches towards the project management practice tend to lack the incorporation of realtime data analytics into robust supply chain systems and engineering decision processes. In this paper, it is hypothesized that, in order to optimize the application of smarter infrastructures, the convergence of business analytics, supply chain resilience strategies, and engineering management methodologies leads to a comprehensive data-driven framework. The research is based on existing information and examples in the world and how the advanced analytics can be applied to evaluate the variables of infrastructure projects and anticipate bottlenecks and subsequently real-time reallocations towards optimal respondents. A hybrid technique of employing the statistical model and predictive modeling and visual business intelligence tools like Power BI and Tableau is developed to develop the framework. It involves project management processes with risk indicators and performance signals at the level of the supply chains in order to provide instanta,neous responsiveness and strategy over time. Outcomes of applied scenarios in the U.S. manufacturing sector indicate quantifiable merits in the delivery time of projects, internal expenditure, risk management, as well as visibility.  The main research findings are associated with the construction of the modular scalable model of the optimization and its appropriateness in a wide range of infrastructure projects and the subsequent demonstration of the practical efficiency of the model use in the high-risk industrial organizations. The study also identifies the pattern of interdisciplinary research capable of transforming decision-making in the era of increasing complexity and worldwide discontinuity using engineering and data analytics science. Further developments contribute to integrating the enhanced version of forecasting based on AI and sustainability indicators in the selected model.