Managing supply chain risks and delays in construction project

The purpose of this paper is to investigate models and methods for managing supply chain risks and delays in construction projects.,The study mainly employs quantitative analysis in order to identify disruptions in construction supply chains. It also uses paradigms of simulation modeling, which are suitable for risk assessment and management. Both qualitative and quantitative data were collected through a literature review and details of specific construction projects, respectively. A dynamic modeling method was used, and the model was provided with an event-based simulation. Simulation modeling was used to measure the performance of the system.,The study shows the benefits of applying the dynamic modeling method to a construction project. Using event-based simulation, it was found that construction delays influence both the magnitude and the probability of disruption. This method contributes to the existing theoretical foundations of risk management practices, since it also considers the time factor. This method supplements the Monte Carlo statistical simulation method, which has no time representation. Using empirical analysis, the study proposes increasing the safety stock of construction materials at the distribution center, so as to mitigate risks in the construction supply chain.,The research considers a single case of a hypothetical construction project. The simulation models represent a simple supply chain with only one supplier. The calculations are based on the current economic scenario, which will of course change over time.,The outcomes of the study show that the introduction of a safety stock of construction materials at the distribution center can prevent supply chain disruption. Since the consideration of risks at all stages of construction supply chain is essential to investors, entrepreneurs and regulatory bodies, the adoption of new approaches for their management during strategic planning of the investment projects is essential.,This dynamic modeling method is used in combination with the Monte Carlo simulation, thus, providing an explicit cause-and-effect dependency over time, as well as a distributed value of outcomes.

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