Abstract
Despite advancing Internet of Things (IoT) technologies, road projects often rely on inaccurate supplier data, making it difficult to determine the cost, quantity, quality, and transportation duration of the needed materials. The wrong choice of material suppliers can lead the supply chain to suffer losses, directly affecting the project’s performance. In this regard, many studies have devised material logistics optimization models for road projects. However, the majority based their decisions on inaccurate or outdated data. This paper studies this gap by introducing a framework that utilizes IoT technologies and smart construction to feed optimization models with accurate and dynamically updated material data. This IoT-powered framework considers only quantitative criteria as input data to the integrated linear programming optimization model, precisely selected suppliers, and optimally calculated costs using MS Excel Solver. The results reveal that the framework is sensitive to any dynamic data updates and can achieve up to 40% material cost savings in real runtime. The paper demonstrates the proposed outline framework with a case study of planning an alternative road between Riyadh and Madinah cities in Saudi Arabia.