Contractor Process Improvement for Enhancing Construction Productivity

Abstract

This paper presents a regression model that relates job site productivity to process improvement initiatives (PIIs) executed both before and during construction. Applied during early project stages, this model helps industry practitioners to predict the expected value of labor productivity based on certain inputs related to preconstruction planning and construction execution. The model demonstrates the strong relationship of project performance to a variety of PIIs including design completeness, definition of a project vision statement, testing oversight, and project manager experience and dedication. The correlational research methodology targeted 75 projects representing approximately $274.53 million in civil construction. The data collection effort considered 45 PIIs (independent variables) using quantitative and qualitative measures. The modeling technique involved the use of multiple linear regression, a method that exploits available data from multiple, independent sources to focus on specific outcomes. The model was developed directly from contractor specific information and subjected to rigorous statistical analysis. The model provides project managers as front line industry practitioners with a deliberate yet practical approach to project management and productivity enhancement. The modeling results include verification analysis and a discussion of the model’s usefulness and limitations.