The precast concrete production process faces uncertainties and risks that reduce the efficiency of the Saudi precast concrete industry. Assessing the risk factors’ interdependence yields better results than considering individual analyses only. The previous precast risk studies did not consider the interdependencies among risk factors concerning different process stages. This paper aims to identify precast risk factors and prioritize their importance in Saudi Arabia. Using a dynamic Bayesian network, the assessment considers the interrelationships among the risk factors and different production stages. The risk factors were collected from previous studies, evaluated, and classified into the five construction stages by performing structured interviews with ten experts. The probabilities of the root risks and conditional probabilities of intermediate and leaf risks were assessed based on the ten experts. Then, a Monte Carlo simulation was utilized to provide the status of these probabilities, which were considered input data for the GeNIe program. The main results revealed that erection productivity is the highest risk, with a probability value of 0.87. In addition, the design schedule’s significance directly impacts production capacity, resulting in a probability value of 0.74. However, the site management risk is low, with a 0.32 probability value. This paper assists practitioners in optimizing construction schedules.

Supplier selection is one of the critical processes that entail multiple complex deliberations. The selection of an appropriate alternative supplier is a highly intricate process, primarily due to there being multiple criteria which are exceptionally subjective. This paper aims to develop a practical framework for choosing a suitable precast supplier by integrating the Value Engineering (VE) concept, Stepwise Weight Assessment Ratio Analysis (SWARA), and the Weighted Aggregated Sum Product Assessment (WASPAS) technique. This paper introduces a novel method to estimate the quality weights of alternative suppliers’ criteria (CQW) by linking factory capacity with the coefficients of the nine significant criteria, computed using principal component analysis (PCA). None of the formal studies make this link directly. The framework’s findings were validated by comparing its results with an expert assessment of five Saudi supplier alternatives. The results revealed that the framework’s results agree with the expert’s judgment. The method of payment criterion received the highest weight, indicating that it was considered the most important of the nine criteria identified. Combining PCA and VE with the WASPAS technique resulted in an unprecedentedly effective selection tool for precast suppliers.