Single Exponential Smoothing Method to Predict Sales Level at Agisa Kenari Printing

This research is based on the Agisa Kenari company, because in carrying out its operations, this company faces obstacles in predicting the level of paper usage that will be sold, resulting in inefficiency in managing paper inventory, which in turn can affect the smooth production and service to customers. Therefore, an effective method is needed to predict paper usage so that the company can manage inventory better and improve operational efficiency. So a more sophisticated prediction system is needed. The system was created with Python programming using the Single Exponential Smoothing (SES) method for calculation. Agisa Kenari is one of the well-known companies in Blitar city engaged in printing, making it interesting to study. The objectives of this study are (1) to describe the application of the Single Exponential Smoothing method to predict the level of use of paper sold at Agisa Kenari Printing, and (2) to describe the accuracy testing of the Single Exponential Smoothing method in predicting the level of use of paper sold at Agisa Kenari Printing using MSE (Mean Squared Error). The approach used in this research is descriptive and quantitative, while the data collection techniques used are observation, interviews, and literature studies. The results of this study are expected to provide practical and applicable solutions for companies in overcoming these obstacles and supporting better decision making. Historical sales data of various paper types, including Sidu brand A4 HVS Paper, Sidu brand F4 HVS Paper, and Sidu brand 80 gram A4 HVS Paper, are used as the basis for forecasting. The forecasting results for July show a demand of 100.02 reams for Sidu brand HVS A4 Paper with a Mean Squared Error (MSE) of 1949.9, 96.2 reams for Sidu brand HVS F4 Paper with an MSE of 1663.9, and 9.8 reams for Sidu brand HVS A4 80 gram Paper with an MSE of 11.0. These results show that the SES method can provide predictions about future paper needs, so that it can help in stock planning and more efficient operations at Agisa Kenari Printing. The implementation of forecasting using the Python program allows for a faster process, simplifying analysis and decision making in inventory management