Forecasting is estimating the magnitude of uncertain future events and provides different results with different supposition. In order to identify the core data pattern of jute bale requirements for yarn production, we examined 10 years' worth of data from Jute Yarn/Twin that were shipped by their member mills Limited. Exponential smoothing and Holt’s methods are commonly used to forecast this output because it provides an adequate result. Selecting the right smoothing constant value is essential for reducing predicting errors. In this work, we created a method for choosing the smoothing constant's ideal value to reduce study errors measured by the mean square error (MSE), mean absolute deviation (MAD), and mean square percent error (MAPE). At the contrary, we discuss research finding result and future possibility so that Jute Mills Limited and similar companies may execute forecasting smoothly and develop the expertise level of the procurement system to stay competitive in the worldwide market.