Machine Learning Applications in Construction Supply Chain Management for Effective Project Delivery

Abstract

The construction industry faces significant challenges such as poor supplier communication and delayed deliveries in supply chain management (SCM), leading to project delays and cost overruns. This study investigates the application of machine learning (ML) to enhance the effectiveness of construction supply chain management for improved project delivery in Nigeria. A comprehensive methodology was employed, beginning with a literature review to identify key SCM factors, followed by a structured survey of 150 construction professionals to gather data on practices and project outcomes. The collected data was analyzed using the Classification Learner app in MATLAB, where various algorithms, including Decision Trees, Support Vector Machines (SVM), and ensemble methods, were trained and validated. Results indicated that Decision Trees (30%) and SVM (26.7%) were the most utilized and effective models for analyzing SCM data. The trained ML model achieved prediction accuracies of up to 90.7% in categorizing factors affecting project delivery. Key influential factors identified include supplier integration, inventory management, and logistics coordination. The study concludes that ML classification techniques are powerful tools for diagnosing SCM inefficiencies and predicting project performance. The findings provide a data-driven framework for construction stakeholders to prioritize SCM strategies, thereby mitigating risks and fostering more effective project delivery.