Dynamic simulation for performance optimization in just-in-time-enabled manufacturing processes

Abstract

Computer-aided simulation has received attention in recent years especially in the area of performance improvement in manufacturing environments. Literature shows that simulation and modeling have not been sufficiently applied in just-in-time (JIT) manufacturing environments in order to identify the impact of key JIT techniques on system performance. In this study, computer-based simulation tools and linear mathematical models have been applied to identify the impact of selected key JIT variables on system performance in automotive component manufacturing environments. A novel approach is suggested to identify and narrow down possible JIT techniques to a meaningful and manageable set. Experimental results show that parameters such as line balancing, multifunction employee, total quality control, and setup time have a significant impact on relevant outputs such as process time and takt time. The outcome is a robust mathematical model, which identifies key JIT drivers in a typical mixed-model assembly line in a given manufacturing environment.