This study involves the implementation of an economic order quantity (EOQ) model which is an inventory control method in a ceramic factory. Two different methods were applied for the calculation of EOQs. The first method is to determine EOQ values using a response surface method-based approach (RSM). The second method uses conventional EOQ calculations. To produce a ceramic product, 281 different and additive materials may be used. First, Pareto (ABC) analysis was performed to determine which of the materials have higher priority. Because of this analysis, the value of 21 items among 281 different materials and additives were compared to the ratio of the total product. The ratio was found to be 70.4% so calculations were made for 21 items. Usage value for every single item for the years 2011, 2012, 2013 and 2014, respectively, were obtained from the company records. Eight different demand forecasting methods were applied to find the amount of the demand in EOQ. As a result of forecasting, the EOQ of the items were calculated by establishing a model. Also, EOQ and RSM calculations for the items were made and both calculation results were compared to each other. Considering the obtained results, it is understood that RSM can be used in EOQ calculations rather than the conventional EOQ model. Also, there are big differences between the EOQ values which were implemented by the company and the values calculated. Because of this work, the RSM-based EOQ approach can be used to decide on the EOQ calculations as a way of improving the system performance.

Improper planning of inventory will affect the factory operating costs, building costs, the cost of loss, and the cost of product defects due to being stored for too long which will eventually become a loss. This research discusses the processing industry which is experiencing lumpy demand. In carrying out the production process, the company has never made plans for future demand, resulting in a waste of message costs due to repeated orders of raw materials ordered to suppliers. This paper contributes to overcoming this issue by simulating future demand by using the Material Requirement Planning (MRP) method with a probabilistic Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ) model. The demand in the coming period is determined using the Autoregressive Integrated Moving Average (ARIMA) method, and an aggregate plan is carried out to determine the regular cost of raw material production and optimal subcontracting. The final analysis states that the calculation of MRP on the selected items using POQ produces the lowest cost for planning S45C-F, SGT-R, and SKD11-R, while SLD-R uses the probabilistic EOQ method.