<![CDATA[Inventory control is an important thing that must be considered by every business actor, especially in the retail sector, too much inventory results in increased and inefficient sales time and can even result inlosses. the need to estimate demand and inventory Stock is very necessary to minimize over stock and also under stock to reduce the risk of loss, the ability of retail business actors to predict demand is certainly very helpful in carrying out good inventory management, utilization of transaction data in a certain amount using machine learning methods can be one approach to see consumer behavior trends. The purpose of this study is to analyze and performance testing the forecasting accuracy, using machine learning approach with the Naive method on sales data transaction in automotive companies and then compare the accuracy between the Stock Order Quantity approach methods used so far. The results of this study indicate forecasting accuracy with a forecasting error of 2% (MAPE), This research tries to analyze the time series data of the spare parts sales transaction, predict the future demand, The results of this study indicate forecasting accuracy with error of 2% (MAPE), This is expected to be an added value in inventory management.]]>
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