Digital twin for production estimation, scheduling and real-time monitoring in offsite construction
Fatima Alsakka, Haitao Yu, Ibrahim El-chami, Farook Hamzeh, Mohamed Al‐Hussein
Abstract
The variability in production operations in offsite construction factories undermines the effectiveness of using average production rates for estimating production time and scheduling. In fact, production schedules based on average rates often exhibit significant deviations from actual production. This study proposes a digital twin for production estimation, scheduling, and real-time monitoring in offsite construction. By integrating computer vision, ultrasonic sensors, machine learning-based prediction models, and 3D simulation, the digital twin continuously collects time data from the shop floor, estimates cycle times, simulates operations, generates production schedules, virtually mirrors operations in real time, and enables the generation of updated schedules based on actual progress. In a case application to a wall framing workstation, the production schedule generated using the digital twin for the framing of wall panels during a work shift achieves an 81% reduction in deviation from actual production time compared to the conventional fixed-rate method commonly used in current practice.