Digital twin technology in apparel industry: potential for rebalancing of manual assembly lines

Abstract

The efficiency of manual assembly lines in the apparel industry is often affected by the dynamic nature of operator performance, which leads to unbalanced lines and associated challenges such as bottlenecks, uneven operator utilization, unmet demand, inventory issues, and lower labor morale. Although line balancing is done at the onset to optimally allocate tasks to workers, most often, rebalancing is necessary, especially due to the uncertainty of task times and operator learning. This research looks at the potential of digital twin technology in effectively rebalancing the line using real-time production data. We employ the well-known Jackson’s 11 task case and demonstrate, through a discrete event simulation model, how real-time rebalancing can improve the performance of the system.