In the entire supply chain, demand planning is one of the crucial aspects of the production planning process. If the demand is not estimated accurately, then it causes revenue loss. Past research has shown forecasting can be used to help the demand planning process for production. However, accurate forecasting from historical data is difficult in today's complex volatile market. Also it is not the only factor that influences the demand planning. Factors, namely, Consumer's shifting interest and buying power also influence the future demand. Hence, this research study focuses on Just-In-Time (JIT) philosophy using a pull control strategy implemented with a Kanban control system to control the inventory flow. Two different product structures, serial product structure and assembly product structure, are considered for this research. Three different methods: the Toyota Production System model, a histogram model and a cost minimization model, have been used to find the number of kanbans that was used in a computer simulated Just-In-Time Kanban System. The simulation model was built to execute the designed scenarios for both the serial and assembly product structure. A test was performed to check the significance effects of various factors on system performance. Results of all three methods were collected and compared to indicate which method provides the most effective way to determine number of kanbans at various conditions. It was inferred that histogram model and cost minimization models are more accurate in calculating the required kanbans for various manufacturing conditions. Method-1 fails to adjust the kanbans when the backordered cost increases or when product structure changes. Among the product structures, serial product structures proved to be effective when Method-2 or Method-3 is used to calculate the kanban numbers for the system. The experimental result data also indicated that the lower container capacity collects more backorders in the system, which increases the inventory cost, than the high container capacity for both serial and assembly product structures.