<![CDATA[This study discusses the inventory of consumable parts at an aircraft maintenance company in Bandung. The problem that occurs is that companies often face stock shortages. Another issue is that the warehouse has excess stock of materials that have an expiration date, which is financially detrimental to the company. To overcome this, the study categorizes materials using the ABC and ADI CV2 analysis methods. After obtaining the demand patterns including smooth demand, intermittent demand, erratic demand, and lumpy demand forecasting is carried out according to the demand pattern using Exponential Smoothing, Croston, Syntetos Boylan Approximation, and Teunter Syntetos Babai. The data used is PT XYZ consumable part data from January 2022 to December 2023. After analysis and testing the level of forecast accuracy using MAD, MSE, and MAPE, the results showed that the Croston, SBA, and TSB forecasting methods each offer their own advantages based on the specific characteristics of the demand pattern applied.]]>
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