Optimization of the supplier selection process in prefabrication using BIM

Prefabrication offers substantial benefits including reduction in construction waste, material waste, energy use, labor demands, and delivery time, and an improvement in project constructability and cost certainty. As the material cost accounts for nearly 70% of the total cost of the prefabrication project, to select a suitable material supplier plays an important role in such a project. The purpose of this study is to present a method for supporting supplier selection of a prefabrication project. The proposed method consists of three parts. First, a list of assessment criteria was established to evaluate the suitability of supplier alternatives. Second, Building Information Modelling (BIM) was adopted to provide sufficient information about the project requirements and suppliers’ profiles, which facilitates the storage and sharing of information. Finally, the Analytic Hierarchy Process (AHP) was used to rank the importance of the assessment criteria and obtain the score of supplier alternatives. The suppliers were ranked based on the total scores. To illustrate how to use the proposed method, it was applied to a real prefabrication project. The proposed method facilitates the supplier selection process by providing sufficient information in an effective way and by improving the understanding of the project requirements

Exploring the trend of New Zealand housing prices to support sustainable development

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

New Zealand Building Project Cost and Its Influential Factors: A Structural Equation Modelling Approach

Construction industry significantly contributes to New Zealand's economic development. However, the delivery of construction projects is usually plagued by cost overruns, which turn potentially successful projects into money-losing ventures, resulting in various other unexpected negative impacts. The objectives of the study were to identify, classify, and assess the impacts of the factors affecting project cost in New Zealand. The proposed research model was examined with structural equation modelling. Recognising the lack of a systematic approach for assessing the influencing factors associated with project cost, this study identified 30 influencing factors from various sources and quantified their relative impacts. The research data were gathered through a questionnaire survey circulated across New Zealand construction industry. A total of 283 responses were received, with a 37% response rate. A model was developed for testing the relationship between project cost and the influential factors. The proposed research model was examined with structural equation modelling (SEM). According to the results of the analysis, market and industry conditions factor has the most significant effect on project cost, while regulatory regime is the second-most significant influencing factor, followed by key stakeholders' perspectives. The findings can improve project cost performance through the identification and evaluation of the cost-influencing factors. The results of such analysis enable industry professionals to better understand cost-related risks in the complex environment