Productivity Forecasting of Newly Added Workers Based on Time-Series Analysis and Site Learning

Abstract

Adding new laborers during construction is usually considered the easiest option to execute when a schedule delay occurs in a construction project. However, determining the proper number of new laborers to add is quite challenging because newly added laborers’ short-term productivity for their first several production cycles could be significantly different from that of existing laborers. While existing studies suggest that newly added laborers’ site-learning may cause such a difference, this process has not been considered when forecasting newly added laborers’ short-term productivity. In this context, this study presents a method that takes into account site-learning effects and the periodic characteristics of newly added laborers’ short-term productivity. The periodic characteristics of productivity are analyzed based on a time-series model of existing laborers’ productivity. Then, the impact of the site-learning effect on the productivity is considered based on existing learning-effect theory. An illustrative example demonstrates the accuracy and usefulness of the presented method. Its results indicate that the consideration of the site-learning effect prevents the frequent and counterproductive underestimation of the required number of newly added laborers in establishing an accelerated recovery schedule.