Drivers of Artificial Intelligence for Optimised Supply Chain Management in Construction Project Delivery

The complexity and interplay of processes, unpredictable market conditions, and resource management create gaps for stakeholders in optimising supply chain management. Unfortunately, these problems continue to affect successful construction project delivery. Therefore, this study explores artificial intelligence drivers for optimising supply chain management in construction project delivery. A structured questionnaire was designed from a literature review and distributed to construction professionals in Durban, KwaZulu-Natal province, South Africa, using systematic random sampling techniques to collect data on leveraging artificial intelligence drivers for optimising supply chain management in construction project delivery. Data from the questionnaire survey were computed and analysed using IBM SPSS version 28 to conduct descriptive analysis (mean and standard deviation) and the Kruskal-Wallis test, while Cronbach&s;s alpha was used to determine data reliability. Thus, the study results establish the significance of eighteen identified artificial intelligence drivers. The findings indicated that automated monitoring and evaluation, big data analytics, procurement data analytics, materials requisition safety and security, AI-guided outsourcing and subletting, knowledge-based systems, data warehouse, strategic partnerships, enterprise resource planning, and cloud-based collaborative tools recorded high significance, artificial intelligence drivers for optimised supply chain management in project delivery. The study provides a better understanding of leveraging artificial intelligence like blockchain, big data analytics, data warehouse, and enterprise resource planning as strategies to meet the increasingly interconnected and digitally driven supply chain linkage to optimise project delivery. The study recommends that understanding artificial intelligence drivers in supply chain management is imperative to optimising supply order cycle management, developing an effective supplier database, integrating automated logistic systems and efficient information feedback systems, and effectively managing people and processes in construction project delivery.