<![CDATA[PT ABC is a company engaged in the distribution of various kinds of building materials, wood construction materials, chemicals, office machines, glass, porcelain, cement, household equip-ment and supplies, and dairy products. The aim of this research is to meet fluctuating product demand. In this article, there are four forecasting methods used based on historical data graphic patterns, namely Naïve, Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. Then, the best forecasting method with the smallest error rate was determined for the X Brush product at PT ABC. From the research results, it is known that the forecasting graph has a trend pattern due to an increase or decrease in data in the long term. The appropriate method for forecasting is using the Exponential Smoothing method with α = 0.1 with a Mean Absolute Deviation (MAD) value of 132.48, Mean Square Error (MSE) of 21981, and Mean Absolute Percentage Error (MAPE) of 61.73%%..]]>
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