Article’s

Material shortages, late deliveries and mis-timed procurement remain among the most persistent causes of schedule overrun in building and infrastructure projects. Traditional material planning still depends on static lead times taken from vendor quotations, bills of quantity that are frozen early, and uniform inventory buffers that treat all materials equally despite different risk levels. This study examines how Artificial Intelligence (AI), Machine Learning (ML), and Explainable AI (XAI) can be integrated into a predictive material planning framework to reduce project delays. Using an empirical illustration based on 1,850 procurement records across twelve material categories, the proposed framework demonstrates significant improvements in lead-time prediction and risk-based inventory allocation. The findings show that AI-driven predictive planning can substantially reduce project delays while improving procurement decision-making and supply chain efficiency.