Modeling Policy and Resource Dynamics in the Construction Sector of Developing Countries: A System Dynamics Approach Using Sudan as a Case Study

TL;DR

This paper develops a system dynamics model for Sudan's construction sector to evaluate policy impacts, showing that regulatory reforms and workforce training significantly improve project outcomes in developing countries.

Contribution

It introduces a hybrid SD-GA modeling framework tailored for analyzing policy and resource dynamics in the construction sector of LDCs, with practical simulation results.

Findings

Regulatory reform reduced project delays by up to 32%.

Workforce training decreased cost overruns by 28%.

Material supply and financial inputs alone had limited impact.

Abstract

Construction industries in developing countries face systemic challenges such as chronic project delays, cost overruns, and regulatory inefficiencies. This paper presents a system dynamics (SD) modeling framework for analyzing policy and resource dynamics within the construction sector in Sudan, with broader applicability to Least Developed Countries (LDCs). The model incorporates key variables related to workforce, material supply, financing, and policy delays, and is calibrated using genetic algorithms (GAs) based on sectoral data and expert input. Simulation results across four policy scenarios indicate that regulatory reform and workforce training are the most effective levers for improving project performance. Specifically, implementing streamlined regulatory procedures reduced project delays by up to 32%, while investment in human capital decreased cost overruns by 28% over a 10-year simulation horizon. In contrast, scenarios focusing solely on material supply or financial inputs produced limited gains without corresponding policy or labor improvements. Sensitivity analysis further revealed that the system is highly responsive to macroeconomic stability and public investment flows. The study demonstrates that a hybrid SD-GA modeling approach offers a valuable decision-support tool for policymakers seeking to improve infrastructure delivery under uncertainty. Recommendations include phased regulatory reforms, targeted capacity building, and integrating modeling tools into strategic infrastructure planning in LDCs.