Modeling and optimization of kanban controlled manufacturing system using improved-Group method of data handling

One of the important sub-components in just-in-time (JIT) systems is number of optimal kanabn cards. Although a number of techniques exist for determining the number of kanbans, they have some shortcomings for operationalizing the kanban setting problem. In this paper, intelligent optimization method replace to old technique in kanban system to generate a structure that described the relationship between the operational factors and the number of kanbans with more accurately. We describe how the improved-Group method data handling algorithm can be used to improve the modeling and prediction accuracy when it is applied to kanban setting in just in time system.