<![CDATA[PT. XYZ is a plywood product manufacturer and exporter. Currently, spare parts inventory management is still done intuitively based on data about previous periods' spare parts needs. As a result, when spare components are required, there is sometimes a shortage. Spare parts inventory management is a challenging subject because it necessitates a quick response to limit damage time and the risk of production machine part obsolescence. Furthermore, spare parts have a distinct demand pattern in which demand does not arise at the same time every time and has a large variance. As a result, it can be classified as intermittent or lumpy demand. So, in this study, spare parts inventory control will be explored, beginning with the classification of spare parts using the ADI-CV approach. The simple moving average approach, single exponential smoothing, Croston's method, Syntetos-Boylan approximation (SBA), and Montecarlo simulation are used to estimate the need for spare components. The performance of forecasting systems is compared by taking five metrics of accuracy into account: A-MAPE, ME, and MSE. The periodic review approach will be used to calculate a safety stock, reorder point, and optimal order quantity based on the results of the best forecast of demands.]]>
Copyrights © 2024
