Reducing Spare Part Inventory Cost with Shortage Elimination through Probabilistic Economic Order Quantity

Abstract

Company Z had an inventory of fast moving spare parts where the demand pattern for these spare parts fluctuated every month and there were several months in which the demand was zero. In addition, company Z did not have a safety stock for these spare parts, so the risk of shortage for these spare parts was quite high. This study aimed to reduce fast moving spare part inventory costs and prevent the risk of shortage by determining the optimal order quantity and re-ordering time. The inventory control model used to determine the optimal order quantity was the Probabilistic Economic Order Quantity (Probabilistic EOQ). This model is appropriate to use in conditions of unstable demand and it requires the calculation of safety stock to prevent shortage.  Research data in the form of data on demand, lead time, purchasing cost, ordering cost, carrying cost, and shortage cost were collected through company documents. The data that had been collected was then processed using an excel spreadsheet. Demand forecasting for the next 12 months was carried out using the Croston method. Forecasting results were used as input in calculating the optimal order quantity on Probabilistic EOQ.  Furthermore, data processing was run by calculating the safety stock and reorder point. The total inventory cost with Probabilistic EOQ was calculated and compared with the company’s total inventory cost to determine whether Probabilistic EOQ is able to create inventory cost reduction. The results showed that Probabilistic EOQ enabled the company to reduce its inventory cost up to 57.85%.