Real-time data-driven discrete-event simulation for garment production lines
Woo‐Kyun Jung, Hyungjung Kim, Young-Chul Park, Jae‐Won Lee, Eun Suk Suh
Abstract
The garment industry, which is traditionally labour-intensive, is facing significant challenges to reduce production costs in the face of material, labour, and infrastructure maintenance cost increases. To address this challenge, various aspects of smart factory-related technologies are being adopted by the garment industry. In this study, we introduce a production line simulation approach based on real-time power monitoring data in the form of garment production workers’ individual task times. Power consumption data for workers’ sewing machines are collected using ICT-based power monitoring devices, and the data are transmitted to a cloud server through a wireless network and processed by an approximation algorithm to extract accurate task time. Power monitoring data are then used as inputs to a discrete-event simulation model to measure the overall productivity, individual productivity, and other key performance indices critical to the garment production line. The simulation results were 18.8% more accurate than the results obtained by the garment production line using a conventional approach. This study demonstrates the potential of smart factory-related technologies and how they can be implemented in different types of industries, such as the garment production industry.