AI-Driven Predictive Material Planning in Construction Projects: A Framework for Reducing Project Delays
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 a truckload of cement and an imported glazing package as if they carried the same risk. This chapter examines how artificial intelligence (AI), machine learning and explainable AI (XAI) can be combined into a single predictive material planning framework, and what that combination actually buys a project team in terms of avoided delay. The argument is developed through a synthesis of the delay-causation and construction supply chain literature, three strategic analysis lenses (PESTEL, Porter's Five Forces and SWOT), and an empirical illustration built on a 1,850-record material procurement dataset covering twelve material categories. Gradient-boosted and random forest models predicted realised delivery lead time with a mean absolute error of 3.61 days, against 6.02 days for the quotation-based baseline, a reduction of roughly forty per cent. Schedule-critical orders were identified with an ROC-AUC of 0.885. SHAP analysis showed that supplier on-time history, planned inventory buffer, transport distance and internal approval cycle time dominated the predictions, while project stage contributed about two per cent of total attribution. A buffer-reallocation simulation demonstrated that redistributing the same total inventory-days according to predicted risk cut unprotected delay exposure by 37.1 per cent without any additional working capital. The chapter contributes an integrated framework linking prediction, explanation and procurement decision-making, and sets out practical guidance for contractors at different levels of digital maturity.