MODELING CONSTRUCTION MANAGEMENT DYNAMICS: A PROXY ESTIMATION OF PERCENT PLANNED COMPLETE

Abstract

The chaotic and complex nature of the construction industry and construction projects hinders maximization of productivity. Certain aspects of lean manufacturing are adaptable to construction with the goal of improving work flow reliability; for example, measuring work flow reliability by Percent Planned Complete (PPC). As more construction projects are being built with lean methodologies, it is important to bring this knowledge into the undergraduate Construction Management (CM) programs so that new graduates have relevant knowledge of emerging trends and can serve as change agents in industry. The purpose of this study was to construct a mathematical estimation of project dynamics through a future-oriented regression model for predicting PPC at various times throughout a project. A hands-on learning activity utilizing small diameter PVC piping materials was developed to collect data for the PPC model. The hands-on activity was executed at various universities using a homogeneous sample of undergraduates in CM courses. A secondary purpose was to assess whether or not participating in the hands-on activity improved learning as compared to a control group which only experienced a lecture. The results indicated various internal and external events experienced by the students were useful in predicting PPC. There is consistent negative direction in the coefficients for the predictors TI, number of Internal events, and TE, number of External events, indicating that for every unit of increase for either TI or TE, there will be a decrease in the future PPC. Furthermore, the hands-on activity did not improve learning in CM students as no statistically significant differences were observed between the test and control groups. A number of approaches are considered for future research to build on the PPC model by utilizing different materials and components, different internal and external events, weighting these events, and collecting data from different industry stakeholders.