CBPM 2025 · Vol. 2025, Issue 1

Predictive Analytics and Stochastic Programming for Construction Resource Optimization in Developing African Economies

Frédéric Mirindi and Derrick Mirindi

Keywords: Artificial Intelligence, Construction Project Management, Resource Optimization, Risk Mitigation, African Infrastructure Development

Abstract

This research investigates the application of AI-driven predictive models for optimizing resource allocation and mitigating economic risks in large-scale construction projects across developing African economies. Through the development of sophisticated mathematical frameworks, including linear programming for resource optimization, multivariate failure prediction models, and stochastic programming for supply chain management, we present a comprehensive approach to addressing the complex challenges facing the African construction sector. The study demonstrates significant improvements in project outcomes, with the predictive maintenance model achieving 91.5% accuracy in equipment failure prediction and the cost estimation framework explaining 81.3% of cost variance. However, implementation challenges including data scarcity, infrastructure limitations, and the need for local expertise development must be carefully addressed. The research provides valuable insights for construction project managers, policymakers, and researchers, contributing to more efficient and sustainable infrastructure development across Africa.