Kanbans play an important role in the information and material flows in a JIT production system. The traditional kanban system with a fixed number of cards does not work satisfactorily in an unstable environment. In the flexible kanban-type pull control mechanism the number of kanbans is allowed to change with respect to the inventory and backorder level. Based on the need for the flexible kanban, a method was proposed by (Husseini, S. M. M., O'Brien, C., and Hosseini, S. T., 2006. A method to enhance volume flexibility in JIT production control. International Journal of Production Economics, 104 (2), 653-665), using an integer linear programming technique, to flexibly determine the number of kanbans for each stage of a JIT production system, minimising total inventory cost for a given planning horizon. Here, the effectiveness of the method proposed by Husseini et al. is examined by a case study and compared with the results for the conventional method of fixed kanban determination. This is also confirmed by a simulation study using artificial neural networks (ANNs). The main aim of this paper is to show the cost advantage for Husseini et al.' s method over the conventional method in fluctuating demand situations, and especially to prove that simulation via ANNs ensures a simplified representation for this method and is time saving.