| description abstract | Successful realization of construction activities requires simultaneous integration of various resource input flows, giving rise to considerable sources of flow variability. Such variability might manifest as schedule variations, which can jeopardize the project performance, especially when using deterministic scheduling. Current scheduling techniques fail to efficiently tackle variability and rely on deterministic approaches. Therefore, this study fills the gap by developing a discrete event simulation model, where activity durations are modeled using beta distributions and program evaluation and review technique assumptions. By applying the Spearman correlation coefficient, activities with higher influence on the schedule were identified, highlighting where to reduce variability. An application example was conducted involving a critical path method (CPM) network containing 11 activities. Two types of waste emerging due to variability were identified as waiting time and variation gaps. Out of the 11 activities in the example network, two sets of critical activities were identified. Results revealed that an 80% reduction in variability in these critical activities led to a 51.9% increase in likelihood of completing the project on schedule, 30% decrease in waiting time, and 28.6% decrease in variation gap. An important implication of this research is that near-critical paths could become critical based on the amount of variability contained in the activities lying on each path. Acquiring such information early on during planning provides proactive, eye-opening insights into potential problematic scheduling areas. The study’s contributions include investigating the variability effect on two types of waste in production and providing project planners with a stochastic approach to manage the hidden waste in production systems; the approach examines the effect of reducing variability on the overall project performance characterized by meeting deadlines, avoiding trade idling (reducing waiting time), and exploring potential opportunities for enhancing performance (reducing variation gaps). | |
