Forecasting Construction Labor Productivity Metrics

This study presents an autoregressive method for forecasting construction labor productivity metrics. Productivity is an essential parameter to monitor progress in construction projects as it can determine whether the project succeeds or fails in terms of cost and time. However, collecting productivity data, or data correlating with productivity, takes time and effort. Furthermore, the collected productivity data offers limited insights into the current and past performance of the construction activities, which can be valuable to project managers but are often too late to act on due to the transitory nature of construction projects. To increase the amount of information available for decision-makers and analyses, this paper investigates the possibility of using forecasting methods to estimate future values of direct work, indirect work, and waste. Four models are developed and evaluated on a dataset collected on Danish construction sites. Being able to forecast these metrics will add value for decision makers.