The Application Of The Decomposition Method In Predicting The Sale Of Building Materials On CV. Laris Baja

Abstract

CV. Laris Baja is a retail store that sells various construction needs and home supplies such as iron, boards, paint, pipes, and cement. The stock recording process is still done manually, which often leads to inaccuracies in inventory control, resulting in overstock or out-of-stock situations. These conditions can cause significant financial losses if not addressed promptly. This study aims to implement the decomposition method to predict the sales of building materials and to design a web-based sales prediction system to support decision-making at CV. Laris Baja. The research applies the decomposition method, with data collected through observation, interviews, and literature studies. The results show that the decomposition method is effective in identifying sales patterns by combining seasonal and trend components to produce reliable sales estimates. The developed prediction system helps the company optimize inventory management, reduce the risk of overstocking or stockouts, and improve operational efficiency. This study emphasizes the importance of systematically utilizing historical data to support data-driven decision-making, while also considering unpredictable external factors. Overall, the study provides a significant contribution to business planning in the building materials industry and can serve as a reference for future research in different scales and contexts