Optimizing quality management in the construction industry: a COQ-based predictive analysis

Abstract

The construction industry faces persistent challenges due to the absence of a standardized financial classification system for quality-related costs, resulting in inefficiencies and project delays. The Cost of Quality (COQ) framework, widely utilized in manufacturing, remains underutilized in construction due to its static assumptions that fail to account for complex interdependencies among quality costs. This study refines COQ classifications and examines the relationship between visible factors (VF) and hidden factors (HF) by using a predictive approach. To fill this gap, a questionnaire survey of 142 construction quality professionals in India was analyzed using SmartPLS 4.0, leading to the development of a predictive COQ model. The Partial Least Squares Structural Equation Modelling (PLS-SEM) results reveal that prevention costs significantly reduce external failure costs (β = 0.465, p < 0.05), while other hypothesized paths, including internal to external failure, were not statistically significant. The model explains 13.2%–22.0% of the variance across COQ components. These findings suggest that prioritizing preventive measures, particularly strategic planning and quality data analysis, is crucial for cost optimization in construction. The study contributes a validated predictive framework and highlights avenues for future research, including regional COQ indexes and AI-enhanced quality monitoring.