Garment production line optimization using production information based on real-time power monitoring data

The implementation of Fourth Industrial Revolution technologies at a manufacturing site requires analysis of the site status based on real-time information, optimization of the processes, and onsite execution. However, in labor-intensive industries, such as the garment-manufacturing industry, it is extremely difficult to develop smart factories based on real-time onsite information because such industries are accustomed to managing labor using empirical expert judgment; moreover, they require rapid production circulation and are dependent on human-related factors. In this study, we developed an optimization simulator using onsite real-time production information that provides decision-making support to maximize the productivity of a garment production plant. As an optimization method, a genetic algorithm was used to incorporate operator relocation into various garment production line variables. Through the developed simulator, field managers can predict and optimize productivity with simple operation in connection with production information. The application of the simulation optimization process to an actual production line in an Indonesian garment factory indicated that the simulator can improve productivity by 34.8%. The results of this study will provide guidance regarding the application of industrial information integration in labor-intensive industries using methods that can systematically support decision-making to achieve optimal productivity.