Characterization of process variability in construction

Variability is a major problem in construction projects. It can negatively affect performance and disrupt production. In the past two decades, a considerable amount of research has been undertaken to select a suitable statistical model that can accurately model this variability. Although the beta distribution has achieved extensive acceptance to model process durations in construction, certain production situations can constrain its modeling capabilities. The limitations are apparent when a coefficient of variation associated with the process variability is higher than 100%. The aim of this research is to explore a reliable probability distribution function that can model the situations in which the beta distribution is not suitable. Data from 73 construction samples corresponding to 25 projects from three different countries were analyzed as a case study by using statistical inference techniques and stochastic simulation. The main finding from this research shows that the Burr distribution was a more accurate statistical function for modeling processes with variability levels between 100 and 150% in comparison to other commonly used distributions. The results enable construction modelers to represent and simulate the process variability with an increased accuracy in a wider range of variability. Accordingly, suitable production strategies can be designed to cope with variability in construction.