Optimal allocation of safety resources in small and medium construction enterprises

Abstract

Construction is a highly vulnerable work sector to safety risks. Small and Medium construction enterprises (SMEs) are often the most vulnerable entities in this sector due to inadequate safety systems. There is an utmost need for a particular safety model adapted to this category of enterprises. This assumption stems from the fact that in SMEs, investments are not directly related to the scope of the project and refer to smaller inputs and therefore, indirect costs such as inspection and regulatory equipment account for higher ratios of the safety investment. The research hypothesized that the optimal safety investment in SMEs is higher than the normative optimum of the entire projects population and reaches the level of 3–5 % of the project scope. The research method used background and characteristics data gathering of the projects through a qualitative questionnaire. Data includes project scope and duration, employees, safety equipment, investment, training, and data regarding construction accidents and near-miss events. The research model was developed based on a sample of 30 SMEs projects. A probabilistic model of safety and accidents was developed based on the survey findings. Monte Carlo simulations are used to analyze the optimal level of safety investments. Based on the empirical variables mentioned above, considering means, standard deviations, and distributions, a series of 5 simulations were carried out. A polynomial regression equation of the five simulation experiments was used to assemble the ideal safety investment equation. The optimal Safety Investment Ratio (SIR) was set at 3.8 %, which is the investment ratio that minimizes the total safety cost, including accident costs. This study provides a novel theoretical framework by identifying an optimal SIR specifically for SMEs projects, highlighting that they require higher investments compared to larger projects to achieve comparable safety levels. Furthermore, this research offers an innovative methodology incorporating comprehensive empirical probabilistic data analysis and simulation analytics delivering insightful and practical understanding of safety investments for construction SMEs.