The Impact of Material Price Fluctuation on Cost Overruns in National Toll Road Infrastructure Projects
DOI:
https://doi.org/10.69855/sipil.v1i2.314Keywords:
Construction Material Price Volatility, Cost Overruns, National Toll Road Projects, Risk Management, Indonesia Predictive Modeling, Infrastructure ProjectsAbstract
Fluctuations in construction material prices significantly challenge infrastructure project management, especially in Indonesia’s national toll road sector from 2023 to 2025. Employing a quantitative approach with purposive sampling of National Strategic Toll Road Projects (PSN), this study investigates the impact of price volatility for core materials (cement, steel, and asphalt) on project cost overruns. The analysis utilizes dual-source data: secondary data from the official Wholesale Price Index (WPI) published by the Central Statistics Agency (BPS) and budget realization data from the Ministry of Public Works and Housing (PUPR)/BPJT. Advanced analytical methods, including Structural Equation Modeling (SEM) to establish causal relationships and Least Squares Support Vector Machine (LSSVM) to develop a predictive model, reveal strong positive correlations between material price fluctuations and cost increases, with steel price volatility having the most pronounced effect. Empirical findings show cement prices increased by approximately 12.5% and steel by 15.3%, leading to cost overruns contributing up to 21% additional project costs. High reliability (Cronbach’s alpha 0.89) and data validity support these findings. Practical implications include the need for real-time price monitoring, adaptive budgeting with contingency funds of 20–25%, flexible procurement contracts, and digital tools like Building Information Modeling for risk mitigation. This research bridges macroeconomic volatility and engineering practice by offering an actionable predictive framework to support fiscal integrity and timely project delivery. Further research should incorporate socio-political variables and enhance predictive analytics using big data.
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