Paper Contents
Abstract
Construction Supply Chain Management (SCM) is essential for driving organizational efficiency and competitiveness. Accurate demand forecasting and efficient shipping are fundamental to a responsive supply chain, yet both face challenges such as demand variability, logistical complexities, and timely delivery requirements. This project explores the use of AI-driven predictive analytics to improve demand forecasting and shipping within construction SCM. By identifying key factorsincluding supplier reliability, lead times, defect rates, and operational flexibilitythe study offers a comprehensive view of the dynamics impacting SCM efficiency. Using Six Sigma and Structural Equation Modeling (SEM) methodologies, this research systematically assesses the role of critical SCM variables. The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework provides a structured approach to identifying key variables and reducing inefficiencies, while SEM enables insight into relationships among these factors. Findings from this study illustrate how predictive analytics, enhanced by AI and IoT technologies, can elevate forecasting accuracy and logistics efficiency. The project ultimately seeks to support a resilient, adaptable, and optimized SCM framework to meet the evolving demands of the construction industry.
Copyright
Copyright © 2025 D BANUPRAKASH. This is an open access article distributed under the Creative Commons Attribution License.
